{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":33,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":33,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"ba4c3db01bc1","filters":{"venue":"The Journal of Machine Learning for Biomedical Imaging"}},"results":[{"id":"W3036790661","doi":"10.59275/j.melba.2020-48g7","title":"COVID-19 Image Data Collection: Prospective Predictions are the Future","year":2020,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Vector Institute; University of Toronto; Université de Montréal","funders":"Compute Canada; Canadian Institute for Advanced Research","keywords":"Metadata; Coronavirus disease 2019 (COVID-19); Data collection; Computer science; Resource (disambiguation); Data science; Code (set theory); Intensive care unit; Medicine; Medical physics; Medical emergency; Information retrieval; Artificial intelligence; Intensive care medicine; World Wide Web; Disease; Pathology","authors":[{"name":"Joseph Cohen","is_ca":true},{"name":"Paul Morrison","is_ca":false},{"name":"Lan Dao","is_ca":true},{"name":"Karsten Roth","is_ca":false},{"name":"Timothy Q. Duong","is_ca":false},{"name":"Marzyeh Ghassemi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03905790863781753,"gpt":0.3697702149244061,"spread":0.3307123062865886,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001702173,0.00124007,0.0005887474,0.002691381,0.0004388491,0.001916143,0.001231697,0.00135374,0.009276813],"category_scores_gemma":[0.007459033,0.0003913401,0.0008197325,0.002473248,0.0004993693,0.00186417,0.001542801,0.001406221,0.01842517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019298,"about_ca_system_score_gemma":0.001203079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009852326,"about_ca_topic_score_gemma":0.01444113,"domain_scores_codex":[0.9987829,0.0001493901,0.0001245257,0.0004286538,0.0003753601,0.0001391168],"domain_scores_gemma":[0.9971149,0.0006052679,0.000227793,0.0008821937,0.0009920358,0.0001777983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006702851,0.000299648,0.04306801,0.001131738,0.0001008271,0.0003546756,0.0002714829,0.004410452,0.006083055,0.003100283,0.8242404,0.1162691],"study_design_scores_gemma":[0.0001812651,0.0002131448,0.08865409,0.0007095876,0.0001327771,0.001149559,0.0008938941,0.05111745,0.02669328,0.01012205,0.8199565,0.0001765071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03416594,0.001782516,0.01909729,0.002571112,0.0006243112,0.0004510465,0.9065393,0.02085229,0.01391622],"genre_scores_gemma":[0.05220877,0.0009363346,0.03580305,0.0003891272,0.00018789,0.000355716,0.9054282,0.001322741,0.003368044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009852326,"threshold_uncertainty_score":0.03103399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3005163827","doi":"10.59275/j.melba.2021-2dcc","title":"The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up","year":2021,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":69,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"EPSRC Centre for Doctoral Training in Medical Imaging; National Institutes of Health; Medical Research Council; Medical Delta; Direktoratet for internasjonalisering og kvalitetsutvikling i høgare utdanning; National Research Foundation Singapore; National Institute of Neurological Disorders and Stroke; National Institute for Health and Care Research; Alzheimer's Society; European Federation of Pharmaceutical Industries and Associations; National Research Foundation; Engineering and Physical Sciences Research Council; UK Research and Innovation; National Institute on Aging; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Portland State University; University College London Hospitals NHS Foundation Trust; U.S. Department of Defense; European Commission; Alzheimer's Disease Neuroimaging Initiative; National Science Foundation","keywords":"Disease; Machine learning; Multivariate statistics; Artificial intelligence; Support vector machine; Medicine; Computer science; Physical medicine and rehabilitation; Internal medicine","authors":[{"name":"Razvan Marinescu","is_ca":false},{"name":"Neil P. Oxtoby","is_ca":false},{"name":"Alexandra L. Young","is_ca":false},{"name":"Esther E. Bron","is_ca":false},{"name":"Arthur W. Toga","is_ca":false},{"name":"Michael W. Weiner","is_ca":false},{"name":"Frederik Barkhof","is_ca":false},{"name":"Nick C. Fox","is_ca":false},{"name":"Arman Eshaghi","is_ca":false},{"name":"Tina Toni","is_ca":false},{"name":"Marcin Salaterski","is_ca":false},{"name":"Veronika Lunina","is_ca":false},{"name":"Manon Ansart","is_ca":false},{"name":"Stanley Durrleman","is_ca":false},{"name":"Pascal Lu","is_ca":false},{"name":"Samuel Iddi","is_ca":false},{"name":"Dan Li","is_ca":false},{"name":"Wesley K. Thompson","is_ca":false},{"name":"Michael Donohue","is_ca":false},{"name":"Aviv Nahon","is_ca":false},{"name":"Yarden Levy","is_ca":false},{"name":"Dan Halbersberg","is_ca":false},{"name":"Mariya Cohen","is_ca":false},{"name":"Huiling Liao","is_ca":false},{"name":"Tengfei Li","is_ca":false},{"name":"Kaixian Yu","is_ca":false},{"name":"Hongtu Zhu","is_ca":false},{"name":"José G. Tamez‐Peña","is_ca":false},{"name":"Aya Ismail","is_ca":false},{"name":"Timothy C. Wood","is_ca":false},{"name":"Héctor Corrada Bravo","is_ca":false},{"name":"Minh Nguyen","is_ca":false},{"name":"Nanbo Sun","is_ca":false},{"name":"Jiashi Feng","is_ca":false},{"name":"B.T. Thomas Yeo","is_ca":false},{"name":"Gang Chen","is_ca":false},{"name":"Ke Qi","is_ca":false},{"name":"Shiyang Chen","is_ca":false},{"name":"Deqiang Qiu","is_ca":false},{"name":"Ionut Buciuman","is_ca":false},{"name":"Alex Kelner","is_ca":false},{"name":"Raluca Maria Pop","is_ca":false},{"name":"Denisa Rimocea","is_ca":false},{"name":"Mostafa Mehdipour Ghazi","is_ca":false},{"name":"Mads Nielsen","is_ca":false},{"name":"Sébastien Ourselin","is_ca":false},{"name":"Lauge Sørensen","is_ca":false},{"name":"Vikram Venkatraghavan","is_ca":false},{"name":"Keli Liu","is_ca":false},{"name":"Christina Rabe","is_ca":false},{"name":"Paul T. Manser","is_ca":false},{"name":"Steven M. Hill","is_ca":false},{"name":"James Howlett","is_ca":false},{"name":"Zhiyue Huang","is_ca":false},{"name":"Steven J. Kiddle","is_ca":false},{"name":"Sach Mukherjee","is_ca":false},{"name":"Anaïs Rouanet","is_ca":false},{"name":"Bernd Taschler","is_ca":false},{"name":"Brian D. M. Tom","is_ca":false},{"name":"Simon R. White","is_ca":false},{"name":"Noel G. Faux","is_ca":false},{"name":"Suman Sedai","is_ca":false},{"name":"Javier de Velasco Oriol","is_ca":false},{"name":"Edgar E. V. Clemente","is_ca":false},{"name":"Karol Estrada","is_ca":false},{"name":"Leon Aksman","is_ca":false},{"name":"André Altmann","is_ca":false},{"name":"Cynthia M. Stonnington","is_ca":false},{"name":"Yalin Wang","is_ca":false},{"name":"Jianfeng Wu","is_ca":false},{"name":"Vivek Devadas","is_ca":false},{"name":"Clémentine Fourrier","is_ca":false},{"name":"Lars Lau Rakêt","is_ca":false},{"name":"Aristeidis Sotiras","is_ca":false},{"name":"Güray Erus","is_ca":false},{"name":"Jimit Doshi","is_ca":false},{"name":"Christos Davatzikos","is_ca":false},{"name":"Jacob W. Vogel","is_ca":true},{"name":"Andrew Doyle","is_ca":true},{"name":"Angela Tam","is_ca":true},{"name":"Alex Diaz-Papkovich","is_ca":true},{"name":"Emmanuel Jammeh","is_ca":false},{"name":"Igor Koval","is_ca":false},{"name":"Paul J. Moore","is_ca":false},{"name":"Terry Lyons","is_ca":false},{"name":"John Gallacher","is_ca":false},{"name":"Jussi Tohka","is_ca":false},{"name":"Robert Ciszek","is_ca":false},{"name":"Bruno Jedynak","is_ca":false},{"name":"K. Pandya","is_ca":false},{"name":"Murat Bilgel","is_ca":false},{"name":"William R. Engels","is_ca":false},{"name":"Joseph B. Cole","is_ca":false},{"name":"Polina Golland","is_ca":false},{"name":"Stefan Klein","is_ca":false},{"name":"Daniel C. Alexander","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01902432039102666,"gpt":0.3133916186062761,"spread":0.2943672982152494,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005955705,0.001770769,0.001620136,0.0005428192,0.0004899739,0.0008557995,0.000971101,0.001639477,0.001112443],"category_scores_gemma":[0.01216657,0.0002244829,0.001665037,0.0002743006,0.0002464231,0.0008469521,0.001425743,0.001413757,0.001004034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004488447,"about_ca_system_score_gemma":0.000721162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00750702,"about_ca_topic_score_gemma":0.007229768,"domain_scores_codex":[0.9986296,0.0005480396,0.00009992485,0.0003881001,0.0002037763,0.0001306823],"domain_scores_gemma":[0.9937235,0.002229839,0.0004184929,0.0009658681,0.001651425,0.001010844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03492322,0.009886133,0.3880143,0.001389799,0.006670491,0.001171145,0.0008951451,0.08000186,0.007664514,0.0005640406,0.1218063,0.347013],"study_design_scores_gemma":[0.003293595,0.03039885,0.541567,0.0006133752,0.003068974,0.001998589,0.001318369,0.3680665,0.01330773,0.003195796,0.03261497,0.0005562791],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689836,0.002608731,0.004671975,0.0007883277,0.0004247004,0.0004096094,0.01910713,0.001111521,0.001894369],"genre_scores_gemma":[0.9327269,0.0005697641,0.01002365,0.0004069602,0.0002191737,0.000496373,0.05299493,0.000149207,0.002413197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00750702,"threshold_uncertainty_score":0.03149718,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362458848","doi":"10.59275/j.melba.2022-354b","title":"QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results","year":2022,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"National Institutes of Health; National Cancer Institute; Instituto Tecnológico de Costa Rica; Natural Sciences and Engineering Research Council of Canada; National Institute of Neurological Disorders and Stroke; Canadian Institute for Advanced Research","keywords":"Benchmarking; Segmentation; Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Task (project management); Ranking (information retrieval); Reliability (semiconductor); Data mining","authors":[{"name":"Raghav Mehta","is_ca":true},{"name":"Angelos Filos","is_ca":false},{"name":"Ujjwal Baid","is_ca":false},{"name":"Chiharu Sako","is_ca":false},{"name":"Richard McKinley","is_ca":false},{"name":"Michael Rebsamen","is_ca":false},{"name":"Katrin Dätwyler","is_ca":false},{"name":"Raphael Meier","is_ca":false},{"name":"Piotr Radojewski","is_ca":false},{"name":"Gowtham Krishnan Murugesan","is_ca":false},{"name":"Sahil Nalawade","is_ca":false},{"name":"Chandan Ganesh","is_ca":false},{"name":"Ben Wagner","is_ca":false},{"name":"Fang Yu","is_ca":false},{"name":"Baowei Fei","is_ca":false},{"name":"Ananth J. Madhuranthakam","is_ca":false},{"name":"Joseph A. Maldjian","is_ca":false},{"name":"Laura Daza","is_ca":false},{"name":"Catalina Gómez","is_ca":false},{"name":"Pablo Arbeláez","is_ca":false},{"name":"Chengliang Dai","is_ca":false},{"name":"Shuo Wang","is_ca":false},{"name":"Hadrien Reynaud","is_ca":false},{"name":"Yuanhan Mo","is_ca":false},{"name":"Elsa D. Angelini","is_ca":false},{"name":"Yike Guo","is_ca":false},{"name":"Wenjia Bai","is_ca":false},{"name":"Subhashis Banerjee","is_ca":false},{"name":"Linmin Pei","is_ca":false},{"name":"Murat Ak","is_ca":false},{"name":"Sarahi Rosas-González","is_ca":false},{"name":"Ilyess Zemmoura","is_ca":false},{"name":"Clovis Tauber","is_ca":false},{"name":"Minh H. Vu","is_ca":false},{"name":"Tufve Nyholm","is_ca":false},{"name":"Tommy Löfstedt","is_ca":false},{"name":"Laura Mora Ballestar","is_ca":false},{"name":"Verónica Vilaplana","is_ca":false},{"name":"Hugh McHugh","is_ca":false},{"name":"Gonzalo D. Maso Talou","is_ca":false},{"name":"Alan Wang","is_ca":false},{"name":"Jay Patel","is_ca":false},{"name":"Ken Chang","is_ca":false},{"name":"Katharina Hoebel","is_ca":false},{"name":"Mishka Gidwani","is_ca":false},{"name":"Nishanth Arun","is_ca":false},{"name":"Sharut Gupta","is_ca":false},{"name":"Mehak Aggarwal","is_ca":false},{"name":"Praveer Singh","is_ca":false},{"name":"Elizabeth R. Gerstner","is_ca":false},{"name":"Jayashree Kalpathy–Cramer","is_ca":false},{"name":"Nicolas Boutry","is_ca":false},{"name":"Alexis Huard","is_ca":false},{"name":"Lasitha Vidyaratne","is_ca":false},{"name":"Md Monibor Rahman","is_ca":false},{"name":"Khan M. Iftekharuddin","is_ca":false},{"name":"Joseph Chazalon","is_ca":false},{"name":"Élodie Puybareau","is_ca":false},{"name":"Guillaume Tochon","is_ca":false},{"name":"Jun Ma","is_ca":false},{"name":"Mariano Cabezas","is_ca":false},{"name":"Xavier Lladó","is_ca":false},{"name":"Arnau Oliver","is_ca":false},{"name":"Liliana Valencia","is_ca":false},{"name":"Sergi Valverde","is_ca":false},{"name":"Mehdi Amian","is_ca":false},{"name":"Mohammadreza Soltaninejad","is_ca":false},{"name":"Andriy Myronenko","is_ca":false},{"name":"Ali Hatamizadeh","is_ca":false},{"name":"Xue Feng","is_ca":false},{"name":"Dou Quan","is_ca":false},{"name":"Nicholas J. Tustison","is_ca":false},{"name":"Craig H. Meyer","is_ca":false},{"name":"Nisarg A. Shah","is_ca":false},{"name":"Sanjay N. Talbar","is_ca":false},{"name":"Marc‐André Weber","is_ca":false},{"name":"Abhishek Mahajan","is_ca":false},{"name":"András Jakab","is_ca":false},{"name":"Roland Wiest","is_ca":false},{"name":"Hassan M. Fathallah‐Shaykh","is_ca":false},{"name":"Arash Nazeri","is_ca":false},{"name":"Mikhail Milchenko","is_ca":false},{"name":"Daniel C. Marcus","is_ca":false},{"name":"Aikaterini Kotrotsou","is_ca":false},{"name":"Rivka R. Colen","is_ca":false},{"name":"John Freymann","is_ca":false},{"name":"Justin Kirby","is_ca":false},{"name":"Christos Davatzikos","is_ca":false},{"name":"Bjoern Menze","is_ca":false},{"name":"Spyridon Bakas","is_ca":false},{"name":"Yarin Gal","is_ca":false},{"name":"Tal Arbel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01864686215268765,"gpt":0.3230956487698615,"spread":0.3044487866171738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03058192,0.004683376,0.00295296,0.007261533,0.001713354,0.005185036,0.004239805,0.005044406,0.005284136],"category_scores_gemma":[0.07986124,0.0006472375,0.002394594,0.003291812,0.002139397,0.003510639,0.005540349,0.002944477,0.003826377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00406472,"about_ca_system_score_gemma":0.003782749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01803184,"about_ca_topic_score_gemma":0.01770958,"domain_scores_codex":[0.9730242,0.009963234,0.002107991,0.003837432,0.009594728,0.001472444],"domain_scores_gemma":[0.9643677,0.01576795,0.002039358,0.004683817,0.01058985,0.002551329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004305622,0.001159511,0.03065875,0.00319033,0.002074703,0.0006123665,0.0005721729,0.2822837,0.0092761,0.009786684,0.264234,0.391846],"study_design_scores_gemma":[0.0006071425,0.001481071,0.01590331,0.0004779166,0.0003155376,0.0006516432,0.0004066793,0.9084808,0.02129158,0.01524152,0.03488247,0.0002603865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4377577,0.03176253,0.3345249,0.01241855,0.006690423,0.003271615,0.05452178,0.06388196,0.05517048],"genre_scores_gemma":[0.7029419,0.002209162,0.1713562,0.002186252,0.0007420994,0.001009443,0.1069734,0.003686096,0.008895233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03058192,"threshold_uncertainty_score":0.1617346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972672210","doi":"10.59275/j.melba.2021-1a1f","title":"PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI","year":2021,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Pipeline (software); Data acquisition; Real-time MRI; Magnetic resonance imaging; Artificial intelligence; Segmentation; Artificial neural network; Iterative reconstruction; Computer vision; Set (abstract data type); Deep learning","authors":[{"name":"Tomer Weiss","is_ca":false},{"name":"Ortal Senouf","is_ca":false},{"name":"Sanketh Vedula","is_ca":false},{"name":"Oleg Michailovich","is_ca":true},{"name":"Michael Zibulevsky","is_ca":false},{"name":"Alex Bronstein","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04342063471811727,"gpt":0.3749468683684789,"spread":0.3315262336503617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009150905,0.0009653121,0.0007504845,0.00040149,0.000308561,0.0007142553,0.001576851,0.001455597,0.005316838],"category_scores_gemma":[0.00425913,0.0006286756,0.0004259481,0.0004213718,0.0009848882,0.001291173,0.001837607,0.0018412,0.001196423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000817865,"about_ca_system_score_gemma":0.001845206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003836846,"about_ca_topic_score_gemma":0.004085198,"domain_scores_codex":[0.9997222,0.00006525335,0.00001227661,0.00005582224,0.0001083437,0.00003614198],"domain_scores_gemma":[0.9992168,0.0003749357,0.00008063138,0.0001127779,0.0001459149,0.0000688955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000324424,0.00008732136,0.0005416421,0.0001473694,0.00003760125,0.0001488924,0.0001452251,0.8237783,0.008110128,0.01843319,0.003825879,0.1444202],"study_design_scores_gemma":[0.00001036943,0.00002714887,0.00003180691,0.000005821686,0.000001806311,0.00001434548,0.00000345921,0.9955015,0.0008099175,0.002927045,0.0006631665,0.000003637862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006305587,0.0001188485,0.9911543,0.0001476869,0.00003620645,0.00004793968,0.00004104161,0.001218998,0.0009293214],"genre_scores_gemma":[0.253152,0.0002014383,0.7398429,0.0002468542,0.00005363603,0.0002572629,0.0004055361,0.000571472,0.005269005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005316838,"threshold_uncertainty_score":0.01778656,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972185346","doi":"10.59275/j.melba.2023-5g54","title":"Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Artificial intelligence; Computer science; Pooling; Deep learning; Pattern recognition (psychology); Pixel; Classifier (UML); Grading (engineering); Computer vision; Machine learning","authors":[{"name":"Jérôme Rony","is_ca":true},{"name":"Soufiane Belharbi","is_ca":true},{"name":"José Dolz","is_ca":true},{"name":"Ismail Ben Ayed","is_ca":true},{"name":"Luke McCaffrey","is_ca":true},{"name":"Éric Granger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03556278911581747,"gpt":0.3614388878623092,"spread":0.3258760987464917,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002001939,0.002472774,0.002383357,0.001958659,0.0003936887,0.00177313,0.003822686,0.002151686,0.002566883],"category_scores_gemma":[0.003898043,0.0009105275,0.001808784,0.002059295,0.0009492564,0.002314983,0.001842624,0.002635331,0.002460628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159502,"about_ca_system_score_gemma":0.001322745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005353687,"about_ca_topic_score_gemma":0.004286783,"domain_scores_codex":[0.9986253,0.0003438168,0.0001390027,0.0004292132,0.0003735123,0.00008911141],"domain_scores_gemma":[0.9981895,0.0009401562,0.0001209348,0.0002317608,0.0004450421,0.0000726681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00015683,0.0002241149,0.00156297,0.001476523,0.0002921582,0.00009512092,0.000148798,0.06297432,0.00315192,0.009422991,0.01398228,0.906512],"study_design_scores_gemma":[0.00003388324,0.0002412984,0.00154912,0.0005227749,0.0001554336,0.0002710477,0.0001311014,0.9298379,0.006266085,0.0268471,0.03407125,0.00007303546],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007654541,0.05777043,0.92633,0.001298233,0.0002886583,0.0001143509,0.0003018497,0.002301163,0.003940859],"genre_scores_gemma":[0.2766947,0.1136197,0.5741293,0.003240973,0.001740846,0.0007552855,0.004794015,0.00159491,0.02343024],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005353687,"threshold_uncertainty_score":0.01064503,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2954679106","doi":"10.59275/j.melba.2021-gfgg","title":"PathologyGAN: Learning deep representations of cancer tissue","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; University of Glasgow","keywords":"Artificial intelligence; Cancer; Computer science; Breast cancer; Colorectal cancer; Deep learning; Feature (linguistics); Feature vector; Pattern recognition (psychology); Stromal cell; Machine learning; Medicine; Pathology; Internal medicine","authors":[{"name":"Adalberto Claudio Quiros","is_ca":false},{"name":"Roderick Murray‐Smith","is_ca":false},{"name":"Ke Yuan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01430746497485931,"gpt":0.3381544727428638,"spread":0.3238470077680045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005291167,0.001006863,0.0004450646,0.0005675112,0.0001631696,0.0006408741,0.001469102,0.001174715,0.002150741],"category_scores_gemma":[0.001414396,0.0004704845,0.0008220526,0.0004580733,0.0004118941,0.0007717362,0.0007867306,0.001468307,0.0008564239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008870744,"about_ca_system_score_gemma":0.0006828399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005763921,"about_ca_topic_score_gemma":0.007961803,"domain_scores_codex":[0.9998249,0.00003995367,0.00000551868,0.00005579934,0.00004117902,0.00003269231],"domain_scores_gemma":[0.9997057,0.0001236187,0.00003400055,0.00005904819,0.00005291148,0.00002467928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002248779,0.000160857,0.003607997,0.000147917,0.0001890091,0.0001788598,0.00006453774,0.8008083,0.0123056,0.005041827,0.01596143,0.1613089],"study_design_scores_gemma":[0.000007637248,0.00002757117,0.0002958961,0.00000805873,0.00000820545,0.00003543285,0.00000523559,0.9944702,0.00176625,0.002579236,0.0007904037,0.000005760677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09678038,0.001512643,0.8857005,0.001040832,0.0002257685,0.0001387567,0.003271959,0.007852297,0.003476891],"genre_scores_gemma":[0.7604912,0.001008429,0.2140582,0.001261024,0.0001215655,0.0003506178,0.01144278,0.0005049367,0.01076137],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005763921,"threshold_uncertainty_score":0.01146072,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3124703593","doi":"10.59275/j.melba.2021-3581","title":"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow","year":2021,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; York University; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Artificial intelligence; Computer science; Image registration; Pixel; Computer vision; Neuroimaging; Affine transformation; Similarity (geometry); Optical flow; Pattern recognition (psychology); Deep learning; Image (mathematics); Mathematics","authors":[{"name":"Sergiu Mocanu","is_ca":true},{"name":"Alan R. Moody","is_ca":true},{"name":"April Khademi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01499695075815004,"gpt":0.3027160138397985,"spread":0.2877190630816484,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837751,0.001483496,0.000996594,0.001860267,0.0005087611,0.001271907,0.002069396,0.001487837,0.003798418],"category_scores_gemma":[0.003839224,0.0008416501,0.001449758,0.001053747,0.0007923646,0.001993989,0.002120906,0.001931363,0.001942994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050904,"about_ca_system_score_gemma":0.001870231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005199722,"about_ca_topic_score_gemma":0.007148638,"domain_scores_codex":[0.9991955,0.0001768885,0.00003866404,0.0002448258,0.0002749715,0.00006912602],"domain_scores_gemma":[0.9993892,0.0001870408,0.0001263004,0.0001522201,0.00009871638,0.00004655975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003026382,0.0001968084,0.001635428,0.0002403408,0.0002448991,0.0002053226,0.0001342017,0.1479232,0.02135742,0.01176858,0.02029051,0.7957007],"study_design_scores_gemma":[0.00004364725,0.0001352064,0.001038085,0.00004390849,0.00003457415,0.0004487802,0.00002480924,0.9561575,0.01728783,0.01150128,0.01322504,0.00005938898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004279242,0.0004171959,0.9879758,0.0002258529,0.00008541789,0.0001301653,0.0002688794,0.00570034,0.0009170852],"genre_scores_gemma":[0.1374667,0.001012916,0.8504133,0.0008094847,0.0001924582,0.0005397912,0.002523153,0.001607384,0.005434664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005199722,"threshold_uncertainty_score":0.012707,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377002285","doi":"10.59275/j.melba.2022-db5c","title":"Label fusion and training methods for reliable representation of inter-rater uncertainty","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Craig H. Neilsen Foundation; Canada First Research Excellence Fund; Nvidia","keywords":"Segmentation; Artificial intelligence; Computer science; Ground truth; Machine learning; Task (project management); Pattern recognition (psychology)","authors":[{"name":"Andréanne Lemay","is_ca":true},{"name":"Charley Gros","is_ca":true},{"name":"Enamundram Naga Karthik","is_ca":true},{"name":"Julien Cohen‐Adad","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03216285539838912,"gpt":0.365834025024052,"spread":0.3336711696256628,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03616056,0.002636533,0.002659104,0.003583015,0.001698452,0.003415501,0.00341544,0.004108119,0.002885934],"category_scores_gemma":[0.07527954,0.0009044221,0.002406852,0.002349879,0.002884374,0.004368023,0.005391567,0.006383794,0.002119108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330921,"about_ca_system_score_gemma":0.002861915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003502611,"about_ca_topic_score_gemma":0.003836736,"domain_scores_codex":[0.9770899,0.009567721,0.00162227,0.006243732,0.004688437,0.0007878682],"domain_scores_gemma":[0.9551587,0.02384221,0.005719242,0.00806762,0.006546684,0.0006655768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001331355,0.0003847301,0.01232362,0.0007659299,0.0008812239,0.0003112263,0.00243342,0.1938944,0.01645514,0.01441867,0.01199382,0.7448064],"study_design_scores_gemma":[0.00009530254,0.0003732448,0.005791005,0.0002625411,0.0002001602,0.0003233802,0.0002422093,0.9310588,0.01997221,0.03483023,0.00669425,0.0001566869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02054622,0.001760517,0.9718748,0.0006387239,0.0001887598,0.0002304473,0.0003486519,0.003174005,0.00123793],"genre_scores_gemma":[0.4363136,0.0009265417,0.553354,0.0009544075,0.0005686476,0.001073017,0.002363544,0.001501301,0.002945029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03616056,"threshold_uncertainty_score":0.1912376,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377004088","doi":"10.59275/j.melba.2022-2d93","title":"Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation","year":2022,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"NeuroRx Research (Canada); McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Genentech; MedDay Pharmaceuticals; Royal Academy of Engineering; International Progressive MS Alliance; Compute Canada; Western Canada Research Grid; Canadian Institute for Advanced Research; Teva Pharmaceutical Industries; Biogen","keywords":"Annotation; Computer science; Context (archaeology); Segmentation; Generalization; Artificial intelligence; Machine learning; Style (visual arts); Ground truth; Automatic image annotation; Image (mathematics); Natural language processing; Image retrieval; Information retrieval; Mathematics; Geography","authors":[{"name":"Brennan Nichyporuk","is_ca":true},{"name":"Jillian Cardinell","is_ca":true},{"name":"Justin Szeto","is_ca":true},{"name":"Raghav Mehta","is_ca":true},{"name":"Jean-Pierre Falet","is_ca":true},{"name":"Douglas L. Arnold","is_ca":true},{"name":"Sotirios A. Tsaftaris","is_ca":false},{"name":"Tal Arbel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01342492877643922,"gpt":0.3338452928445346,"spread":0.3204203640680953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03502148,0.001450678,0.001509959,0.001097187,0.001191042,0.00290618,0.00249384,0.002805149,0.001105523],"category_scores_gemma":[0.08141629,0.0007995028,0.001702116,0.0009101861,0.00430267,0.004489143,0.004152125,0.004117706,0.0004985157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657149,"about_ca_system_score_gemma":0.00131562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005276982,"about_ca_topic_score_gemma":0.005454166,"domain_scores_codex":[0.9901363,0.004668042,0.0006206401,0.003104534,0.001031146,0.0004393206],"domain_scores_gemma":[0.9344419,0.04060575,0.004697033,0.01592934,0.003416711,0.0009093525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001729812,0.0004110339,0.08120061,0.0004829589,0.001043325,0.0003581431,0.002805527,0.4812798,0.03334701,0.02130557,0.004463293,0.371573],"study_design_scores_gemma":[0.0000716409,0.000292155,0.01640739,0.0001086007,0.0001636723,0.0002430579,0.0001675203,0.9269413,0.009296971,0.04438981,0.001824084,0.00009374992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2726225,0.001417563,0.7179239,0.002228423,0.0002265082,0.0003067329,0.0003951022,0.002001583,0.002877616],"genre_scores_gemma":[0.8897205,0.0003925394,0.1052797,0.001357167,0.0002511021,0.0001993553,0.000753648,0.000485246,0.001560696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03502148,"threshold_uncertainty_score":0.1852135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386246031","doi":"10.59275/j.melba.2023-553a","title":"Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Segmentation; Artificial intelligence; Computer science; Deep learning; Categorical variable; Pattern recognition (psychology); Binary classification; Code (set theory); Machine learning","authors":[{"name":"Amirhossein Rasoulian","is_ca":true},{"name":"Soorena Salari","is_ca":true},{"name":"Yiming Xiao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0198977324395852,"gpt":0.3105897827932063,"spread":0.2906920503536211,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007838916,0.0008846691,0.0008530554,0.0006639406,0.0003543132,0.0010482,0.001307056,0.001245683,0.001814441],"category_scores_gemma":[0.00238112,0.0003726938,0.0009709168,0.0004721369,0.000659746,0.0009314904,0.001304239,0.001571435,0.0008404636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007566296,"about_ca_system_score_gemma":0.001155941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004872737,"about_ca_topic_score_gemma":0.006630184,"domain_scores_codex":[0.9997125,0.00006967173,0.00001621667,0.00009815605,0.00006144212,0.00004194471],"domain_scores_gemma":[0.999361,0.0002407859,0.00006773456,0.0001065094,0.0001561003,0.00006789268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007438428,0.0002808597,0.004863304,0.0002337937,0.000179665,0.0004051414,0.0002950782,0.3885659,0.03141991,0.006147839,0.01302669,0.553838],"study_design_scores_gemma":[0.00001188586,0.000061949,0.0004502232,0.00001050318,0.00001546795,0.00007763873,0.0000177458,0.9885133,0.006453219,0.00352052,0.0008554276,0.00001218022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06674355,0.000596857,0.9232264,0.0006248672,0.0001146551,0.0001516547,0.0004073764,0.005982506,0.002152126],"genre_scores_gemma":[0.6991656,0.000447788,0.2894604,0.0009451467,0.0001617866,0.0001987017,0.002009829,0.0007071598,0.006903528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004872737,"threshold_uncertainty_score":0.009688735,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411696885","doi":"10.59275/j.melba.2025-f6fg","title":"BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Medicine","authors":[{"name":"Anahita Fathi Kazerooni","is_ca":false},{"name":"Xinyang Liu","is_ca":false},{"name":"Debanjan Haldar","is_ca":false},{"name":"Zhifan Jiang","is_ca":false},{"name":"Anna Zapaishchykova","is_ca":false},{"name":"Julija Pavaine","is_ca":false},{"name":"Lubdha M. Shah","is_ca":false},{"name":"Blaise V. Jones","is_ca":false},{"name":"Nakul Sheth","is_ca":false},{"name":"Sanjay P. Prabhu","is_ca":false},{"name":"Aaron S. McAllister","is_ca":false},{"name":"Wenxin Tu","is_ca":false},{"name":"Khanak Nandolia","is_ca":false},{"name":"Andres Fernandez Rodriguez","is_ca":false},{"name":"Ibraheem S Shaikh","is_ca":false},{"name":"Mariana Sanchez-Montano","is_ca":false},{"name":"Hollie Anne Lai","is_ca":false},{"name":"Maruf Adewole","is_ca":false},{"name":"Jake Albrecht","is_ca":false},{"name":"Udunna Anazodo","is_ca":true},{"name":"Hannah Anderson","is_ca":false},{"name":"Syed Muhammed Anwar","is_ca":false},{"name":"Alejandro Aristizábal","is_ca":false},{"name":"Sina Bagheri","is_ca":false},{"name":"Ujjwal Baid","is_ca":false},{"name":"Timothy Bergquist","is_ca":false},{"name":"Austin J. Borja","is_ca":false},{"name":"Evan Calabrese","is_ca":false},{"name":"Verena Chung","is_ca":false},{"name":"Gian-Marco Conte","is_ca":false},{"name":"James A. Eddy","is_ca":false},{"name":"Ivan Ezhov","is_ca":false},{"name":"Ariana Familiar","is_ca":false},{"name":"Keyvan Farahani","is_ca":false},{"name":"Deep Gandhi","is_ca":false},{"name":"Anurag Gottipati","is_ca":false},{"name":"Shuvanjan Haldar","is_ca":false},{"name":"Juan Eugenio Iglesias","is_ca":false},{"name":"Anastasia Janas","is_ca":false},{"name":"Elaine Elaine","is_ca":false},{"name":"Alexandros Karargyris","is_ca":false},{"name":"Hasan Kassem","is_ca":false},{"name":"Neda Khalili","is_ca":false},{"name":"Florian Kofler","is_ca":false},{"name":"Dominic LaBella","is_ca":false},{"name":"Koen Van Leemput","is_ca":false},{"name":"Hongwei Li","is_ca":false},{"name":"Nazanin Maleki","is_ca":false},{"name":"Zeke Meier","is_ca":false},{"name":"Bjoern Menze","is_ca":false},{"name":"Ahmed W. Moawad","is_ca":false},{"name":"Sarthak Pati","is_ca":false},{"name":"Marie Piraud","is_ca":false},{"name":"Tina Young Poussaint","is_ca":false},{"name":"Zachary J. Reitman","is_ca":false},{"name":"Jeffrey D. Rudie","is_ca":false},{"name":"Rachit Saluja","is_ca":false},{"name":"Micah Sheller","is_ca":false},{"name":"Russell Takeshi Shinohara","is_ca":false},{"name":"Karthik Viswanathan","is_ca":false},{"name":"Chunhao Wang","is_ca":false},{"name":"Benedikt Wiestler","is_ca":false},{"name":"Walter F. Wiggins","is_ca":false},{"name":"Christos Davatzikos","is_ca":false},{"name":"Phillip B. Storm","is_ca":false},{"name":"Miriam Bornhorst","is_ca":false},{"name":"Roger Packer","is_ca":false},{"name":"Trent R. Hummel","is_ca":false},{"name":"Peter de Blank","is_ca":false},{"name":"Lindsey M. Hoffman","is_ca":false},{"name":"Mariam Aboian","is_ca":false},{"name":"Ali Nabavizadeh","is_ca":false},{"name":"Jeffrey B. Ware","is_ca":false},{"name":"Benjamin H. Kann","is_ca":false},{"name":"Brian Rood","is_ca":false},{"name":"Adam J. Resnick","is_ca":false},{"name":"Spyridon Bakas","is_ca":false},{"name":"Arastoo Vossough","is_ca":false},{"name":"Marius George Linguraru","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01205186063516096,"gpt":0.3193921706241968,"spread":0.3073403099890358,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00696462,0.003070227,0.001710912,0.002574122,0.0008632737,0.001843012,0.001753171,0.002839555,0.002250119],"category_scores_gemma":[0.01924263,0.0004063339,0.002150023,0.001696516,0.001058551,0.001624017,0.002739467,0.002056403,0.00191575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001989208,"about_ca_system_score_gemma":0.002989985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02423837,"about_ca_topic_score_gemma":0.02928967,"domain_scores_codex":[0.9955136,0.00143798,0.0002591149,0.0008946779,0.001423683,0.0004709101],"domain_scores_gemma":[0.9921773,0.00312435,0.000432555,0.0008630183,0.002376001,0.001026848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004246285,0.001688004,0.03441199,0.001837355,0.00174986,0.001286149,0.0005154512,0.1683526,0.008488688,0.003968692,0.4382744,0.3351806],"study_design_scores_gemma":[0.001633608,0.00317425,0.08507471,0.0007798692,0.001075971,0.00414565,0.001638629,0.6155202,0.03920339,0.01435629,0.2330054,0.0003920232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6482859,0.02074819,0.1120488,0.0180469,0.005884459,0.002542775,0.1285915,0.02394017,0.03991131],"genre_scores_gemma":[0.5610347,0.003571889,0.1238056,0.003764508,0.001184535,0.001105396,0.2891086,0.003465838,0.01295881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02423837,"threshold_uncertainty_score":0.04819459,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4377004358","doi":"10.59275/j.melba.2022-a1cc","title":"Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation","year":2022,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; CRIS Cancer Foundation; University of Manchester; National Cancer Institute; University College London; National Institutes of Health; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Engineering and Physical Sciences Research Council; Prostate Cancer Foundation; University of Cambridge; Cancer Research UK; Knight Cancer Institute, Oregon Health and Science University; Oregon Health and Science University","keywords":"Task (project management); Computer science; Image quality; Quality (philosophy); Artificial intelligence; Segmentation; Image (mathematics)","authors":[{"name":"Shaheer U. Saeed","is_ca":false},{"name":"Yan Wen","is_ca":false},{"name":"Yunguan Fu","is_ca":false},{"name":"Francesco Giganti","is_ca":false},{"name":"Qianye Yang","is_ca":false},{"name":"Zachary M. C. Baum","is_ca":false},{"name":"Mirabela Rusu","is_ca":false},{"name":"Richard E. Fan","is_ca":false},{"name":"Geoffrey A. Sonn","is_ca":false},{"name":"Mark Emberton","is_ca":false},{"name":"Dean C. Barratt","is_ca":false},{"name":"Yipeng Hu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0166994048962141,"gpt":0.3522393019801811,"spread":0.335539897083967,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003009195,0.0009543656,0.0007151943,0.001508233,0.0002488694,0.001282423,0.000784399,0.001205742,0.001037921],"category_scores_gemma":[0.009051863,0.0003258292,0.0008641956,0.0009569429,0.0006249826,0.001101021,0.00150323,0.0009904484,0.0002851055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004698663,"about_ca_system_score_gemma":0.0007023581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694074,"about_ca_topic_score_gemma":0.002238129,"domain_scores_codex":[0.9990056,0.0002970036,0.00008712782,0.0002004478,0.0003340524,0.00007571139],"domain_scores_gemma":[0.9964479,0.001471733,0.0005708418,0.0004630972,0.0008533215,0.0001929251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001084863,0.0004103626,0.01201587,0.0007680279,0.0003918195,0.0003727323,0.0003803334,0.1854666,0.1463929,0.003495591,0.001664087,0.647557],"study_design_scores_gemma":[0.00003862922,0.0005337397,0.01872133,0.00007055164,0.000182473,0.000721509,0.00008496157,0.9262739,0.04519865,0.005477802,0.002607194,0.0000893059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09962641,0.002109783,0.8942025,0.000427276,0.0000849655,0.0002034109,0.0001750196,0.001592562,0.001578025],"genre_scores_gemma":[0.6427907,0.0008023178,0.3539053,0.0002313348,0.0001159055,0.0001150484,0.0003106561,0.0003330935,0.001395564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003009195,"threshold_uncertainty_score":0.01591438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390873488","doi":"10.59275/j.melba.2024-b87a","title":"Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Agence Nationale de la Recherche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Grand Équipement National De Calcul Intensif; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Autoencoder; Artificial intelligence; Computer science; Generative model; Anomaly detection; Deep learning; Pattern recognition (psychology); Ground truth; Anomaly (physics); Image (mathematics); Generative grammar; Computer vision","authors":[{"name":"Ravi Hassanaly","is_ca":false},{"name":"Camille Brianceau","is_ca":false},{"name":"Maëlys Solal","is_ca":false},{"name":"Olivier Colliot","is_ca":false},{"name":"Ninon Burgos","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01622369334265318,"gpt":0.2913899809458296,"spread":0.2751662876031765,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003753421,0.001157546,0.0006569445,0.000827322,0.0002169034,0.0008274166,0.0009762131,0.001641152,0.0009125359],"category_scores_gemma":[0.009054468,0.0003909049,0.0009026229,0.0003335754,0.0009211698,0.0006002028,0.001026023,0.0009119857,0.0002366274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008655182,"about_ca_system_score_gemma":0.0007486294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005635804,"about_ca_topic_score_gemma":0.003990821,"domain_scores_codex":[0.9990906,0.0004654888,0.00005119222,0.0001511359,0.000163812,0.00007783403],"domain_scores_gemma":[0.9966615,0.002375855,0.0001787871,0.0003279456,0.0003243445,0.0001315573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007130292,0.0001928153,0.004703229,0.0002481116,0.0002579797,0.0002898445,0.0001269171,0.907967,0.01447374,0.002632191,0.001114997,0.06728014],"study_design_scores_gemma":[0.00001275762,0.00008486119,0.0005317327,0.00001257828,0.00001459422,0.0001026355,0.00001251984,0.9922113,0.006020315,0.000767824,0.0002189425,0.000009872459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.473193,0.002124942,0.5169575,0.001145207,0.0001767228,0.0002378535,0.0007156447,0.003049877,0.002399222],"genre_scores_gemma":[0.8851135,0.0005038008,0.1116247,0.0002423179,0.00003227011,0.00006531516,0.001053487,0.0002789499,0.001085647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005635804,"threshold_uncertainty_score":0.01985025,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389481639","doi":"10.59275/j.melba.2023-g3f8","title":"Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Boston Children's Hospital; National Institutes of Health; National Science Foundation","keywords":"Segmentation; Placenta; Artificial intelligence; Fetus; Pattern recognition (psychology); Hyperoxia; Computer science; Medicine; Pregnancy; Biology; Internal medicine","authors":[{"name":"S. Mazdak Abulnaga","is_ca":false},{"name":"Neel Dey","is_ca":false},{"name":"Sean I. Young","is_ca":false},{"name":"Eileen Pan","is_ca":false},{"name":"Katherine Hobgood","is_ca":false},{"name":"Clinton J. Wang","is_ca":false},{"name":"P. Ellen Grant","is_ca":false},{"name":"Esra Abacı Türk","is_ca":false},{"name":"Polina Golland","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01220282339589988,"gpt":0.2869925574705007,"spread":0.2747897340746008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001463397,0.0004948708,0.0005262563,0.0008570281,0.0002791167,0.0008515019,0.0005568728,0.0009204092,0.0005494357],"category_scores_gemma":[0.003541565,0.0002941349,0.000567802,0.0004440438,0.0004762777,0.000598225,0.0005590977,0.0006076495,0.00031999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006577633,"about_ca_system_score_gemma":0.0007804255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003534984,"about_ca_topic_score_gemma":0.004255967,"domain_scores_codex":[0.999768,0.00007364145,0.00001432275,0.00006993529,0.00004655262,0.00002752045],"domain_scores_gemma":[0.9994728,0.0002301515,0.0001130611,0.00005631687,0.00008808857,0.00003965116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007441733,0.0002090256,0.03060385,0.0001845103,0.0001579774,0.0008018773,0.0004857659,0.4848115,0.1202401,0.004539831,0.003775031,0.3534465],"study_design_scores_gemma":[0.00001232115,0.0001134552,0.01086511,0.00002458487,0.00002720446,0.0003724995,0.00006749731,0.9669522,0.01777698,0.002653264,0.001116782,0.00001808358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3508574,0.001010163,0.6441486,0.0007510049,0.00005421073,0.0001150429,0.0003968113,0.001221877,0.001444771],"genre_scores_gemma":[0.8478336,0.0007197816,0.1479483,0.0001928524,0.00006213924,0.0001143176,0.0008919322,0.0002247288,0.002012427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003534984,"threshold_uncertainty_score":0.007739305,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3156072047","doi":"10.59275/j.melba.2021-df47","title":"Adversarial Robust Training of Deep Learning MRI Reconstruction Models","year":2021,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institutes of Health; National Institute of Arthritis and Musculoskeletal and Skin Diseases; University of California, San Francisco; York University","keywords":"Computer science; Artificial intelligence; Iterative reconstruction; Deep learning; Generalization; Machine learning; Workflow; Set (abstract data type); Fidelity; 3D reconstruction; Code (set theory); Pattern recognition (psychology); Computer vision; Mathematics","authors":[{"name":"Francesco Calivá","is_ca":false},{"name":"Kaiyang Cheng","is_ca":false},{"name":"Rutwik Shah","is_ca":false},{"name":"Valentina Pedoia","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0130247580965794,"gpt":0.2346587911582449,"spread":0.2216340330616655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093819,0.001183658,0.0007018938,0.0004225453,0.0002618501,0.0006990656,0.001416917,0.001290078,0.001917879],"category_scores_gemma":[0.007464437,0.0005685317,0.0006845148,0.0002925285,0.001145996,0.0009025184,0.001590266,0.002347314,0.0006626068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009155283,"about_ca_system_score_gemma":0.0008522657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002856977,"about_ca_topic_score_gemma":0.003556152,"domain_scores_codex":[0.999316,0.0002612779,0.00003201128,0.0001540793,0.0001405709,0.00009603821],"domain_scores_gemma":[0.9976076,0.00146477,0.0002214872,0.0003479197,0.000274242,0.0000840399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001700896,0.00006371539,0.001342879,0.00007404533,0.0000517874,0.0001101739,0.00004092788,0.929676,0.005172132,0.004921609,0.002250092,0.05612662],"study_design_scores_gemma":[0.000004012805,0.00001955214,0.00005813985,0.000005895602,0.000002945734,0.00001639581,0.000002657966,0.9968034,0.001643082,0.001241732,0.0001995618,0.000002614594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06099696,0.0005572301,0.9325289,0.0006357513,0.00009475771,0.00008228139,0.0001999542,0.002303568,0.002600517],"genre_scores_gemma":[0.804849,0.0003133823,0.1884135,0.0005735494,0.00006148392,0.0001694736,0.0007642903,0.0002724888,0.004582876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002856977,"threshold_uncertainty_score":0.01107329,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411667236","doi":"10.59275/j.melba.2025-9bd3","title":"The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Hospital for Sick Children; SickKids Foundation; Montreal Neurological Institute and Hospital; University of Toronto; Windsor Regional Hospital; University of Calgary; McGill University; Centre Hospitalier de l’Université de Montréal; Queen's University","funders":"National Cancer Institute; National Institutes of Health; Children's Hospital of Philadelphia; Yale University","keywords":"Segmentation; Brain metastasis; Medicine; Magnetic resonance imaging; Internal medicine; Metastasis; Artificial intelligence; Computer science; Radiology; Cancer","authors":[{"name":"Ahmed W. Moawad","is_ca":false},{"name":"Anastasia Janas","is_ca":false},{"name":"Ujjwal Baid","is_ca":false},{"name":"Divya Ramakrishnan","is_ca":false},{"name":"Rachit Saluja","is_ca":false},{"name":"Nader A. Fawzy","is_ca":false},{"name":"Nazanin Maleki","is_ca":false},{"name":"Leon Jekel","is_ca":false},{"name":"Nikolay Yordanov","is_ca":false},{"name":"Pascal Fehringer","is_ca":false},{"name":"Athanasios Gkampenis","is_ca":false},{"name":"Raisa Amiruddin","is_ca":false},{"name":"Amirreza Manteghinejad","is_ca":false},{"name":"Maruf Adewole","is_ca":false},{"name":"Jake Albrecht","is_ca":false},{"name":"Udunna Anazodo","is_ca":true},{"name":"Sanjay Aneja","is_ca":false},{"name":"Timothy Bergquist","is_ca":false},{"name":"Veronica Chiang","is_ca":false},{"name":"Verena Chung","is_ca":false},{"name":"Gian Marco Conte","is_ca":false},{"name":"Farouk Dako","is_ca":false},{"name":"J. Mark Eddy","is_ca":false},{"name":"Ivan Ezhov","is_ca":false},{"name":"Keyvan Farahani","is_ca":false},{"name":"Juan Eugenio Iglesias","is_ca":false},{"name":"Zhifan Jiang","is_ca":false},{"name":"Elaine Johanson","is_ca":false},{"name":"Anahita Fathi Kazerooni","is_ca":false},{"name":"Florian Kofler","is_ca":false},{"name":"Kiril Krantchev","is_ca":false},{"name":"Dominic LaBella","is_ca":false},{"name":"Koen Van Leemput","is_ca":false},{"name":"Hongwei Li","is_ca":false},{"name":"Marius George Linguraru","is_ca":false},{"name":"Xinyang Liu","is_ca":false},{"name":"Zeke Meier","is_ca":false},{"name":"Bjoern Menze","is_ca":false},{"name":"Harrison Moy","is_ca":false},{"name":"Klara Osenberg","is_ca":false},{"name":"Marie Piraud","is_ca":false},{"name":"Zachary Reitman","is_ca":false},{"name":"Russell Takeshi Shinohara","is_ca":false},{"name":"Chunhao Wang","is_ca":false},{"name":"Benedikt Wiestler","is_ca":false},{"name":"Walter F. Wiggins","is_ca":false},{"name":"Umber Shafique","is_ca":false},{"name":"Klara Willms","is_ca":false},{"name":"Arman Avesta","is_ca":false},{"name":"Khaled Bousabarah","is_ca":false},{"name":"Satrajit Chakrabarty","is_ca":false},{"name":"Nicolò Gennaro","is_ca":false},{"name":"Wolfgang Holler","is_ca":false},{"name":"Manpreet Kaur","is_ca":false},{"name":"Pamela LaMontagne","is_ca":false},{"name":"MingDe Lin","is_ca":false},{"name":"Jan Lost","is_ca":false},{"name":"Daniel S. Marcus","is_ca":false},{"name":"R Maresca","is_ca":false},{"name":"Sarah Merkaj","is_ca":false},{"name":"Gabriel Cassinelli Pedersen","is_ca":false},{"name":"Marc von Reppert","is_ca":false},{"name":"Aristeidis Sotiras","is_ca":false},{"name":"Oleg M. Teytelboym","is_ca":false},{"name":"Niklas Tillmans","is_ca":false},{"name":"Malte Westerhoff","is_ca":false},{"name":"Ayda Youssef","is_ca":false},{"name":"Devon Godfrey","is_ca":false},{"name":"Scott Floyd","is_ca":false},{"name":"Andreas M. Rauschecker","is_ca":false},{"name":"Javier Villanueva-Meyer","is_ca":false},{"name":"Irada Pflüger","is_ca":false},{"name":"Jaeyoung Cho","is_ca":false},{"name":"Martin Bendszus","is_ca":false},{"name":"Gianluca Brugnara","is_ca":false},{"name":"Justin Cramer","is_ca":false},{"name":"Gloria J. Guzman Perez-Carillo","is_ca":false},{"name":"Derek R. Johnson","is_ca":false},{"name":"Anthony Kam","is_ca":false},{"name":"Benjamin Y. M. Kwan","is_ca":true},{"name":"Lillian M. Lai","is_ca":false},{"name":"Neil Lall","is_ca":false},{"name":"Fatima Memon","is_ca":false},{"name":"Mark Krycia","is_ca":false},{"name":"Satya Narayana Patro","is_ca":false},{"name":"Bojan Petrović","is_ca":false},{"name":"Tiffany Y. So","is_ca":false},{"name":"Gerard Thompson","is_ca":false},{"name":"Lei Wu","is_ca":false},{"name":"E. Brooke Schrickel","is_ca":false},{"name":"Anu Bansal","is_ca":false},{"name":"Frederik Barkhof","is_ca":false},{"name":"Cristina Besada","is_ca":false},{"name":"Sammy Chu","is_ca":false},{"name":"T. Jason Druzgal","is_ca":false},{"name":"Alexandru Dusoi","is_ca":false},{"name":"Luciano Farage","is_ca":false},{"name":"Fabrício Stewan Feltrin","is_ca":false},{"name":"Amy Fong","is_ca":false},{"name":"Steve H. Fung","is_ca":false},{"name":"R. Ian Gray","is_ca":false},{"name":"Ichiro Ikuta","is_ca":false},{"name":"Michael Iv","is_ca":false},{"name":"Alida A. Postma","is_ca":false},{"name":"Amit Mahajan","is_ca":false},{"name":"David Joyner","is_ca":false},{"name":"Chase Krumpelman","is_ca":false},{"name":"Laurent Létourneau‐Guillon","is_ca":true},{"name":"Christie M. Lincoln","is_ca":false},{"name":"Máté E. Maros","is_ca":false},{"name":"Elka Miller","is_ca":true},{"name":"Fanny Morón","is_ca":false},{"name":"Esther A. Nimchinsky","is_ca":false},{"name":"Ö. Özsarlak","is_ca":false},{"name":"Uresh Patel","is_ca":false},{"name":"Saurabh Rohatgi","is_ca":false},{"name":"Atin Saha","is_ca":false},{"name":"Anousheh Sayah","is_ca":false},{"name":"Eric D. Schwartz","is_ca":false},{"name":"Robert Shih","is_ca":false},{"name":"Mark S. Shiroishi","is_ca":false},{"name":"Juan E. Small","is_ca":false},{"name":"Manoj Tanwar","is_ca":false},{"name":"Jewels Valerie","is_ca":false},{"name":"Brent D. Weinberg","is_ca":false},{"name":"Matthew L. White","is_ca":false},{"name":"Robert J. Young","is_ca":false},{"name":"Vahe M. Zohrabian","is_ca":false},{"name":"Aynur Azizova","is_ca":false},{"name":"Melanie Brüßeler","is_ca":false},{"name":"Mohanad Ghonim","is_ca":false},{"name":"Abdullah Okar","is_ca":false},{"name":"L. Pasquini","is_ca":false},{"name":"Yasaman Sharifi","is_ca":false},{"name":"Gagandeep Singh","is_ca":false},{"name":"Nico Sollmann","is_ca":false},{"name":"Theodora Soumala","is_ca":false},{"name":"Mahsa Taherzadeh","is_ca":false},{"name":"Philipp Kickingereder","is_ca":false},{"name":"Martha Foltyn","is_ca":false},{"name":"Ajay Malhotra","is_ca":false},{"name":"Aly Abayazeed","is_ca":false},{"name":"Francesco Dellepiane","is_ca":false},{"name":"Philipp Lohmann","is_ca":false},{"name":"Víctor M. Pérez‐García","is_ca":false},{"name":"Hesham Elhalawani","is_ca":false},{"name":"Maria Correia de Verdier","is_ca":false},{"name":"Sanaria Al-Rubaiey","is_ca":false},{"name":"Rui Duarte Armindo","is_ca":false},{"name":"Kholod Ashraf","is_ca":false},{"name":"Moamen Mostafa","is_ca":false},{"name":"Mohamed Badawy","is_ca":false},{"name":"Jeroen Bisschop","is_ca":false},{"name":"Nima Broomand Lomer","is_ca":false},{"name":"Jan Bukatz","is_ca":false},{"name":"Jim Chen","is_ca":true},{"name":"Petra Cimflová","is_ca":true},{"name":"Felix Corr","is_ca":false},{"name":"Alexis Crawley","is_ca":false},{"name":"Lisa Deptula","is_ca":false},{"name":"Tasneem Elakhdar","is_ca":false},{"name":"Islam H. Shawali","is_ca":false},{"name":"Shahriar Faghani","is_ca":false},{"name":"Alexandra Frick","is_ca":false},{"name":"Vaibhav Gulati","is_ca":false},{"name":"Muhammad Ammar Haider","is_ca":false},{"name":"Fátima Hierro","is_ca":false},{"name":"Rasmus Holmboe Dahl","is_ca":false},{"name":"Sarah Maria Jacobs","is_ca":false},{"name":"Kuang-chun Jim Hsieh","is_ca":false},{"name":"Sedat Giray Kandemirli","is_ca":false},{"name":"Katharina Kersting","is_ca":false},{"name":"Laura Kida","is_ca":false},{"name":"Sofia Kollia","is_ca":false},{"name":"Ioannis Koukoulithras","is_ca":false},{"name":"Xiao Li","is_ca":false},{"name":"Ahmed Abouelatta","is_ca":false},{"name":"Aya Mansour","is_ca":false},{"name":"Ruxandra-Catrinel Maria-Zamfirescu","is_ca":false},{"name":"Marcela Marsiglia","is_ca":false},{"name":"Yohana Sarahi Mateo-Camacho","is_ca":false},{"name":"Mark J. McArthur","is_ca":false},{"name":"Olivia McDonnell","is_ca":false},{"name":"M McHugh","is_ca":false},{"name":"Mana Moassefi","is_ca":false},{"name":"Samah Mostafa Morsi","is_ca":false},{"name":"Alexander Munteanu","is_ca":false},{"name":"Khanak Nandolia","is_ca":true},{"name":"Syed Raza Naqvi","is_ca":true},{"name":"Yalda Nikanpour","is_ca":false},{"name":"Mostafa Alnoury","is_ca":false},{"name":"Abdullah Mohamed Aly Nouh","is_ca":false},{"name":"Francesca Pappafava","is_ca":false},{"name":"Markand D. Patel","is_ca":false},{"name":"S. Petrucci","is_ca":false},{"name":"Eric Rawie","is_ca":false},{"name":"Scott B. Raymond","is_ca":false},{"name":"Borna Roohani","is_ca":false},{"name":"Sadeq Sabouhi","is_ca":false},{"name":"Laura Sánchez‐García","is_ca":false},{"name":"Zoe Shaked","is_ca":false},{"name":"Pokhraj P Suthar","is_ca":false},{"name":"Talissa A. Altes","is_ca":false},{"name":"Edvin Isufi","is_ca":false},{"name":"Yaseen Dhemesh","is_ca":false},{"name":"Jaime Gass","is_ca":false},{"name":"Jonathan Thacker","is_ca":false},{"name":"Abdul Tarabishy","is_ca":false},{"name":"Benjamin Turner","is_ca":false},{"name":"Sebastiano Vacca","is_ca":false},{"name":"George K. Vilanilam","is_ca":false},{"name":"Daniel Warren","is_ca":false},{"name":"David Weiss","is_ca":false},{"name":"Fikadu Worede","is_ca":false},{"name":"Sara Yousry","is_ca":false},{"name":"Wondwossen Lerebo","is_ca":false},{"name":"Alejandro Aristizábal","is_ca":false},{"name":"Alexandros Karargyris","is_ca":false},{"name":"Hasan Kassem","is_ca":false},{"name":"Sarthak Pati","is_ca":false},{"name":"Micah Sheller","is_ca":false},{"name":"Katherine E. Link","is_ca":false},{"name":"Evan Calabrese","is_ca":false},{"name":"Nourel Hoda Tahon","is_ca":false},{"name":"Ayman Nada","is_ca":false},{"name":"Yury Velichko","is_ca":false},{"name":"Spyridon Bakas","is_ca":false},{"name":"Jeffrey D. Rudie","is_ca":false},{"name":"Mariam Aboian","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0108620056299499,"gpt":0.3296952720967598,"spread":0.3188332664668099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02237092,0.002653545,0.001827452,0.004595896,0.001937745,0.004613376,0.004355018,0.005002372,0.003166683],"category_scores_gemma":[0.05327276,0.001159071,0.003095703,0.002804311,0.001586209,0.001794455,0.005870989,0.002512933,0.003671481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002795917,"about_ca_system_score_gemma":0.007243478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01374095,"about_ca_topic_score_gemma":0.02788245,"domain_scores_codex":[0.9891522,0.004240026,0.00152455,0.002212527,0.002308114,0.0005625791],"domain_scores_gemma":[0.9767506,0.01124873,0.001638954,0.00403653,0.004955026,0.001370172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004453991,0.0005863101,0.04494556,0.01169694,0.003243482,0.002524527,0.002435715,0.05754833,0.03791997,0.007473072,0.4238214,0.4033507],"study_design_scores_gemma":[0.002256157,0.002753903,0.09026621,0.004767155,0.002861646,0.01181233,0.003609665,0.216627,0.1011631,0.03676334,0.5262766,0.0008429753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4562162,0.03759332,0.2499144,0.01844892,0.005451776,0.006738369,0.1499239,0.04929353,0.02641952],"genre_scores_gemma":[0.3726697,0.004708998,0.3165368,0.004737456,0.0009166954,0.003175501,0.2797326,0.01038719,0.007135012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02237092,"threshold_uncertainty_score":0.1183102,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408171770","doi":"10.59275/j.melba.2025-bea1","title":"Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; University of Toronto; McGill University; University of Ottawa; Montreal Neurological Institute and Hospital","funders":"","keywords":"Meningioma; Medicine; Radiology","authors":[{"name":"Dominic LaBella","is_ca":false},{"name":"Ujjwal Baid","is_ca":false},{"name":"Omaditya Khanna","is_ca":false},{"name":"Shan McBurney-Lin","is_ca":false},{"name":"Ryan McLean","is_ca":false},{"name":"Pierre Nedelec","is_ca":false},{"name":"Arif Rashid","is_ca":false},{"name":"Nourel Hoda Tahon","is_ca":false},{"name":"Talissa A. Altes","is_ca":false},{"name":"Radhika Bhalerao","is_ca":false},{"name":"Yaseen Dhemesh","is_ca":false},{"name":"Devon Godfrey","is_ca":false},{"name":"Fathi Hilal","is_ca":false},{"name":"Scott Floyd","is_ca":false},{"name":"Anastasia Janas","is_ca":false},{"name":"Anahita Fathi Kazerooni","is_ca":false},{"name":"John P. Kirkpatrick","is_ca":false},{"name":"Collin Kent","is_ca":false},{"name":"Florian Kofler","is_ca":false},{"name":"Kevin Leu","is_ca":false},{"name":"Nazanin Maleki","is_ca":false},{"name":"Bjoern Menze","is_ca":false},{"name":"Maxence Pajot","is_ca":false},{"name":"Zachary J. Reitman","is_ca":false},{"name":"Jeffrey D. Rudie","is_ca":false},{"name":"Rachit Saluja","is_ca":false},{"name":"Yury Velichko","is_ca":false},{"name":"Chunhao Wang","is_ca":false},{"name":"Pranav Warman","is_ca":false},{"name":"Maruf Adewole","is_ca":false},{"name":"Jake Albrecht","is_ca":false},{"name":"Udunna Anazodo","is_ca":true},{"name":"Syed Muhammad Anwar","is_ca":false},{"name":"Timothy Bergquist","is_ca":false},{"name":"Sully Francis Chen","is_ca":false},{"name":"Verena Chung","is_ca":false},{"name":"Rong Chai","is_ca":false},{"name":"Gian-Marco Conte","is_ca":false},{"name":"Farouk Dako","is_ca":false},{"name":"J. Mark Eddy","is_ca":false},{"name":"Ivan Ezhov","is_ca":false},{"name":"Nastaran Khalili","is_ca":false},{"name":"Juan Eugenio Iglesias","is_ca":false},{"name":"Zhifan Jiang","is_ca":false},{"name":"Elaine Johanson","is_ca":false},{"name":"Koen Van Leemput","is_ca":false},{"name":"Hongwei Li","is_ca":false},{"name":"Marius George Linguraru","is_ca":false},{"name":"Xinyang Liu","is_ca":false},{"name":"Aria Mahtabfar","is_ca":false},{"name":"Zeke Meier","is_ca":false},{"name":"Ahmed W. Moawad","is_ca":false},{"name":"John Mongan","is_ca":false},{"name":"Marie Piraud","is_ca":false},{"name":"Russell Takeshi Shinohara","is_ca":false},{"name":"Walter F. Wiggins","is_ca":false},{"name":"Aly Abayazeed","is_ca":false},{"name":"Rachel Akinola","is_ca":false},{"name":"András Jakab","is_ca":false},{"name":"Michel Bilello","is_ca":false},{"name":"Maria Correia de Verdier","is_ca":false},{"name":"Priscila Crivellaro","is_ca":true},{"name":"Christos Davatzikos","is_ca":false},{"name":"Keyvan Farahani","is_ca":false},{"name":"John Freymann","is_ca":false},{"name":"Christopher P. Hess","is_ca":false},{"name":"Raymond Y. Huang","is_ca":false},{"name":"Philipp Lohmann","is_ca":false},{"name":"Mana Moassefi","is_ca":false},{"name":"Matthew W. Pease","is_ca":false},{"name":"Phillipp Vollmuth","is_ca":false},{"name":"Nico Sollmann","is_ca":false},{"name":"David Diffley","is_ca":false},{"name":"Khanak Nandolia","is_ca":false},{"name":"Daniel Warren","is_ca":false},{"name":"Ali Hussain","is_ca":false},{"name":"Pascal Fehringer","is_ca":false},{"name":"Yulia Bronstein","is_ca":false},{"name":"Lisa Deptula","is_ca":false},{"name":"Evan G. Stein","is_ca":false},{"name":"Mahsa Taherzadeh","is_ca":false},{"name":"Eduardo Portela de Oliveira","is_ca":true},{"name":"Aoife Haughey","is_ca":true},{"name":"Marinos Kontzialis","is_ca":false},{"name":"Luca Saba","is_ca":false},{"name":"Benjamin Turner","is_ca":false},{"name":"Melanie Brüßeler","is_ca":false},{"name":"Shehbaz Ansari","is_ca":false},{"name":"Athanasios Gkampenis","is_ca":false},{"name":"David Maximilian Weiss","is_ca":false},{"name":"Aya Mansour","is_ca":false},{"name":"Islam H. Shawali","is_ca":false},{"name":"Nikolay Yordanov","is_ca":false},{"name":"Joel M. Stein","is_ca":false},{"name":"Roula Hourani","is_ca":false},{"name":"Mohammed Yahya Moshebah","is_ca":false},{"name":"Ahmed Magdy Abouelatta","is_ca":false},{"name":"Tanvir Rizvi","is_ca":false},{"name":"Klara Willms","is_ca":false},{"name":"Dann C. Martin","is_ca":false},{"name":"Abdullah Okar","is_ca":false},{"name":"Gennaro D’Anna","is_ca":false},{"name":"Ahmed Taha","is_ca":true},{"name":"Yasaman Sharifi","is_ca":false},{"name":"Shahriar Faghani","is_ca":false},{"name":"Dominic Kite","is_ca":false},{"name":"Marco C. Pinho","is_ca":false},{"name":"Muhammad Ammar Haider","is_ca":false},{"name":"Alejandro Aristizábal","is_ca":false},{"name":"Alexandros Karargyris","is_ca":false},{"name":"Hasan Kassem","is_ca":false},{"name":"Sarthak Pati","is_ca":false},{"name":"Micah Sheller","is_ca":false},{"name":"Michelle Alonso‐Basanta","is_ca":false},{"name":"Javier Villanueva-Meyer","is_ca":false},{"name":"Andreas M. Rauschecker","is_ca":false},{"name":"Ayman Nada","is_ca":false},{"name":"Mariam Aboian","is_ca":false},{"name":"Adam E. Flanders","is_ca":false},{"name":"Benedikt Wiestler","is_ca":false},{"name":"Spyridon Bakas","is_ca":false},{"name":"Evan Calabrese","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01167730608787076,"gpt":0.3034014316426765,"spread":0.2917241255548058,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004740178,0.002539458,0.001300884,0.001943607,0.001255547,0.001969459,0.002469177,0.002357339,0.002555211],"category_scores_gemma":[0.01218117,0.0005339374,0.001655625,0.001410256,0.0009009989,0.0011444,0.002416845,0.001695794,0.003358391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002880797,"about_ca_system_score_gemma":0.002631877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234292,"about_ca_topic_score_gemma":0.04104444,"domain_scores_codex":[0.9950669,0.001026927,0.0002416911,0.00109541,0.002138589,0.0004304249],"domain_scores_gemma":[0.9943663,0.001613267,0.0003145302,0.0007546462,0.002292462,0.0006586527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.004627257,0.002831119,0.04075944,0.003364348,0.001625283,0.002661613,0.001315926,0.143673,0.05076104,0.00502505,0.4078926,0.3354634],"study_design_scores_gemma":[0.001128845,0.002590664,0.06568246,0.0003582168,0.0005275146,0.003389657,0.001853075,0.6961723,0.06959108,0.009844078,0.1485676,0.0002945444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8127232,0.006299654,0.06868453,0.005166654,0.001894778,0.002645689,0.05626978,0.01781242,0.02850344],"genre_scores_gemma":[0.5872027,0.001014511,0.1424653,0.002335324,0.0005258821,0.001602718,0.2443727,0.002965282,0.01751556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0234292,"threshold_uncertainty_score":0.04658568,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3003953721","doi":"10.59275/j.melba.2021-8678","title":"A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Nvidia; F. Hoffmann-La Roche; Alzheimer's Society; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Segmentation; Data mining; Machine learning; Heteroscedasticity; Uncertainty quantification; Robustness (evolution); Image quality; Noise (video); Quality (philosophy); Pattern recognition (psychology); Image (mathematics)","authors":[{"name":"Richard Shaw","is_ca":false},{"name":"Carole H. Sudre","is_ca":false},{"name":"Sébastien Ourselin","is_ca":false},{"name":"M. Jorge Cardoso","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02763975736512384,"gpt":0.3395162783419846,"spread":0.3118765209768607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00341095,0.001291654,0.0007973486,0.00070744,0.0003048175,0.001137314,0.001675366,0.001498876,0.001197507],"category_scores_gemma":[0.01258945,0.0007011309,0.001015079,0.0004696874,0.00219129,0.001858973,0.001716322,0.002313192,0.0002957163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449325,"about_ca_system_score_gemma":0.0007746812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003332942,"about_ca_topic_score_gemma":0.002983239,"domain_scores_codex":[0.9985182,0.0004752142,0.00007895628,0.0004476138,0.0003654643,0.000114684],"domain_scores_gemma":[0.9942404,0.003564828,0.0009442467,0.0005130055,0.0006341404,0.0001033738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008051618,0.00002131707,0.001135113,0.00005563741,0.00005419175,0.00007407855,0.00009049474,0.9594308,0.004509275,0.01609993,0.0004671795,0.01798156],"study_design_scores_gemma":[0.000003341757,0.00002419447,0.0004766119,0.0000101683,0.00001320672,0.00003614777,0.000003518528,0.990513,0.001434827,0.007221269,0.0002508205,0.00001290574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01835609,0.0001845628,0.979916,0.0002647007,0.00002614994,0.00003107996,0.0001066807,0.0001732222,0.0009415229],"genre_scores_gemma":[0.8826954,0.0004565245,0.1104415,0.0003182708,0.0001149753,0.0002368188,0.0004011003,0.000150882,0.00518449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00341095,"threshold_uncertainty_score":0.01803905,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4395067675","doi":"10.59275/j.melba.2024-4dg2","title":"A voxel-level approach to brain age prediction: A method to assess regional brain aging","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hotchkiss Brain Institute; Ontario Brain Institute; University of Calgary","funders":"","keywords":"Brain aging; Voxel; Aging brain; Computer science; Psychology; Artificial intelligence; Neuroscience; Cognition","authors":[{"name":"Neha Gianchandani","is_ca":true},{"name":"Mahsa Dibaji","is_ca":true},{"name":"Johanna M. Ospel","is_ca":true},{"name":"Fernando Vega","is_ca":true},{"name":"Mariana Bento","is_ca":true},{"name":"M. Ethan MacDonald","is_ca":true},{"name":"Roberto Souza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1009177687035369,"gpt":0.3692185245557048,"spread":0.2683007558521679,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264918,0.000839398,0.0007898069,0.001751875,0.0004091616,0.001081738,0.001282844,0.001432747,0.00297925],"category_scores_gemma":[0.004500353,0.0003901677,0.001129827,0.001216153,0.0005664336,0.001522401,0.00133516,0.001530188,0.001373005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005440634,"about_ca_system_score_gemma":0.0009640731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004376941,"about_ca_topic_score_gemma":0.005915369,"domain_scores_codex":[0.9996688,0.00007811174,0.00001811182,0.0001279969,0.00006577273,0.00004122741],"domain_scores_gemma":[0.9991744,0.0002862735,0.0001378173,0.0001450302,0.0001976379,0.00005888649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006358797,0.0002371592,0.02188589,0.0004050738,0.000441183,0.0003657344,0.0004161349,0.2569585,0.0320004,0.02164995,0.02204126,0.6429629],"study_design_scores_gemma":[0.00002275437,0.0001027234,0.00547548,0.00004596952,0.00009317191,0.0004318925,0.00006004913,0.9462889,0.009032638,0.03161744,0.006782298,0.00004662749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01293467,0.0005701994,0.982435,0.0003114558,0.0000750129,0.0000662854,0.0007493608,0.001994761,0.0008631735],"genre_scores_gemma":[0.392616,0.001119462,0.5976462,0.0003878403,0.0002949715,0.0004012771,0.002062371,0.0007727314,0.004699156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004376941,"threshold_uncertainty_score":0.009966552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283735188","doi":"10.59275/j.melba.2022-7175","title":"A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations","year":2022,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Agence Nationale de la Recherche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Computer science; Differential privacy; Probabilistic logic; Realization (probability); Bayesian probability; Latent variable; Data mining; Code (set theory); Federated learning; Machine learning; Artificial intelligence; Statistics","authors":[{"name":"Irène Balelli","is_ca":false},{"name":"Santiago Silva","is_ca":false},{"name":"Marco Lorenzi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05570277757738716,"gpt":0.3366063187090567,"spread":0.2809035411316695,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007217564,0.0009435235,0.001658728,0.0009312144,0.0006086865,0.00247791,0.004269793,0.001911411,0.001919488],"category_scores_gemma":[0.01528892,0.00106692,0.001694077,0.001602321,0.002194313,0.003764578,0.00368454,0.003178107,0.0006065025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002114918,"about_ca_system_score_gemma":0.002226284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00354178,"about_ca_topic_score_gemma":0.00346006,"domain_scores_codex":[0.996567,0.001530604,0.0001333544,0.0007984309,0.0007432314,0.0002273424],"domain_scores_gemma":[0.9924515,0.003892755,0.0008735033,0.001808393,0.000721384,0.0002525413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001510204,0.00005617526,0.001544808,0.00007674618,0.0001197851,0.000144451,0.0001578034,0.8457505,0.002022051,0.09723331,0.001901658,0.05084169],"study_design_scores_gemma":[0.000007842135,0.00001562399,0.0001302055,0.000007240862,0.00001025731,0.00003687228,0.000008907083,0.95718,0.0005124917,0.04141592,0.0006640636,0.00001057156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001697013,0.00005343313,0.9977581,0.0001367379,0.000008225114,0.00001138643,0.00007193245,0.0001081122,0.0001550384],"genre_scores_gemma":[0.4742067,0.0006406393,0.5171456,0.0005576209,0.00022648,0.0003888582,0.0009613779,0.0002935704,0.005579082],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007217564,"threshold_uncertainty_score":0.03817064,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3157026294","doi":"10.59275/j.melba.2021-a6fd","title":"Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"European Regional Development Fund; European Commission","keywords":"Uncertainty quantification; Computer science; Regression; Inference; Calibration; Bayesian probability; Dropout (neural networks); Machine learning; Sensitivity analysis; Predictive inference; Artificial intelligence; Monte Carlo method; Bayesian inference; Econometrics; Statistics; Uncertainty analysis; Mathematics; Frequentist inference","authors":[{"name":"Max-Heinrich Laves","is_ca":false},{"name":"Sontje Ihler","is_ca":false},{"name":"Jacob Friedemann Fast","is_ca":false},{"name":"Lüder A. Kahrs","is_ca":true},{"name":"Tobias Ortmaier","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009173376499591528,"gpt":0.2787949627953283,"spread":0.2696215862957367,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006130112,0.001281649,0.001060606,0.001334891,0.000673808,0.002728951,0.001623126,0.001749811,0.001576093],"category_scores_gemma":[0.03350002,0.0008181078,0.001050178,0.001059367,0.002214216,0.003116482,0.003669359,0.003518553,0.0003880811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407846,"about_ca_system_score_gemma":0.001611461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003034797,"about_ca_topic_score_gemma":0.003540644,"domain_scores_codex":[0.9974805,0.0008941215,0.0001551412,0.0005685843,0.0007516799,0.0001500022],"domain_scores_gemma":[0.9898964,0.006704377,0.001010881,0.001468399,0.0007217101,0.0001982801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003200214,0.00007428907,0.005691398,0.0003221875,0.0001860356,0.0002501653,0.0003758385,0.7476279,0.01414751,0.05958018,0.002920897,0.1685036],"study_design_scores_gemma":[0.00001053979,0.00003280048,0.0009590492,0.00006265699,0.00002199979,0.0001098169,0.00002128414,0.9473728,0.00831393,0.04160627,0.001454082,0.00003482185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01808207,0.000481147,0.9792097,0.0005013048,0.00004258628,0.0000219375,0.000105524,0.0007055702,0.0008502401],"genre_scores_gemma":[0.7146167,0.0009247962,0.2802052,0.0006526781,0.0001896597,0.0001336016,0.0006093102,0.0007959448,0.001872142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006130112,"threshold_uncertainty_score":0.0324195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406412026","doi":"10.59275/j.melba.2024-24gc","title":"Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"National Cancer Institute; National Institutes of Health","keywords":"Reproducibility; Medicine; Radiology; Thresholding; Feature (linguistics); Artificial intelligence; Nuclear medicine; Computer science; Mathematics; Statistics; Image (mathematics)","authors":[{"name":"Jacob Peoples","is_ca":true},{"name":"Mohammad Hamghalam","is_ca":true},{"name":"Imani James","is_ca":true},{"name":"Maida Wasim","is_ca":true},{"name":"Natalie Gangai","is_ca":true},{"name":"Hyunseon C. Kang","is_ca":true},{"name":"X. John Rong","is_ca":false},{"name":"Yun Shin Chun","is_ca":true},{"name":"Richard Kinh Gian","is_ca":true},{"name":"Amber L. Simpson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007204905557761505,"gpt":0.287949067085769,"spread":0.2807441615280075,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003334142,0.0004536985,0.0004487038,0.00140606,0.0001703582,0.0008967555,0.0003594895,0.0004834512,0.0003524069],"category_scores_gemma":[0.01497614,0.0002938532,0.0006887199,0.0007829634,0.0004530371,0.000442543,0.0004832029,0.0003798579,0.0002091063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249618,"about_ca_system_score_gemma":0.0002364738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732447,"about_ca_topic_score_gemma":0.00205545,"domain_scores_codex":[0.998896,0.0003526268,0.0001685977,0.0002633839,0.0002181408,0.0001012197],"domain_scores_gemma":[0.9913753,0.003975461,0.002232432,0.00153483,0.0007188086,0.0001631941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001050715,0.0000428009,0.9567719,0.00004414835,0.0004372546,0.000341172,0.0001507812,0.006374893,0.0169898,0.00004495706,0.000188308,0.01756327],"study_design_scores_gemma":[0.00002705075,0.0002016331,0.967117,0.00001581896,0.0002345836,0.001104805,0.0001100262,0.02063487,0.01009639,0.0001793868,0.0002467338,0.00003183147],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971968,0.0001627507,0.002293725,0.00001750014,0.000003711439,0.000007434287,0.0001828161,0.00004024683,0.0000951508],"genre_scores_gemma":[0.9986538,0.00002763227,0.0008545449,0.000006542453,0.000006385463,0.000003869261,0.0004055961,0.00001085134,0.00003072086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003334142,"threshold_uncertainty_score":0.0176329,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4376880401","doi":"10.59275/j.melba.2023-7e96","title":"Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robustness (evolution); Coronavirus disease 2019 (COVID-19); Fuse (electrical); Artificial intelligence; Feature (linguistics); Generalization; Data mining; Pattern recognition (psychology); Medicine; Mathematics; Pathology; Infectious disease (medical specialty); Disease","authors":[{"name":"Qi Xiao","is_ca":true},{"name":"David J. Foran","is_ca":true},{"name":"John L. Nosher","is_ca":true},{"name":"Ilker Hacihaliloglu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04386742789201088,"gpt":0.3721641631122394,"spread":0.3282967352202286,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001181081,0.0008946242,0.001023573,0.001034798,0.0005363186,0.0005296344,0.001280528,0.001111799,0.001313503],"category_scores_gemma":[0.002018585,0.0003138659,0.0009254712,0.0007574683,0.0004374205,0.0009808948,0.001175955,0.0009675656,0.000379821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000814811,"about_ca_system_score_gemma":0.0009237379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01333901,"about_ca_topic_score_gemma":0.009103877,"domain_scores_codex":[0.9995875,0.00008441469,0.00002329688,0.0001299895,0.00008890699,0.00008582281],"domain_scores_gemma":[0.9994639,0.0001914341,0.00006110945,0.00004981974,0.0001900021,0.00004373485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007044197,0.000435513,0.008454629,0.0001254625,0.0002711609,0.0005400301,0.0002057331,0.4074596,0.01814204,0.002044674,0.005298065,0.5563186],"study_design_scores_gemma":[0.000008157931,0.00005717671,0.0009768436,0.000004865446,0.00003778649,0.00005322953,0.00001232582,0.9956118,0.001884319,0.000980105,0.0003660922,0.000007352856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1805058,0.002613567,0.8097075,0.001130576,0.0002248119,0.0001968217,0.0003265023,0.00205625,0.003238166],"genre_scores_gemma":[0.9250177,0.0004568471,0.07118876,0.0003215115,0.0001422538,0.0001010788,0.0003501237,0.00003862893,0.002383035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01333901,"threshold_uncertainty_score":0.02652276,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400480052","doi":"10.59275/j.melba.2024-3d4e","title":"Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts","year":2024,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of Toronto","funders":"National Institute of Child Health and Human Development; National Health and Medical Research Council; Medical Research Council; Fédération pour la Recherche sur le Cerveau; Centre Hospitalier Universitaire Vaudois; Université de Lausanne; Hôpitaux Universitaires de Genève; Fondation pour la Recherche Médicale; National Natural Science Foundation of China; Mission Interministérielle de Lutte Contre les Drogues et les Conduites Addictives; Science Foundation Ireland; École Polytechnique Fédérale de Lausanne; Agence Nationale de la Recherche; European Commission; Institut national de recherche en informatique et en automatique (INRIA); Deutsche Forschungsgemeinschaft; Université de Genève; King's College London; Bundesministerium für Bildung und Forschung; National Institute for Health and Care Research; National Institutes of Health; Fondation de l'Avenir pour la Recherche Médicale Appliquée; Institut National de la Santé et de la Recherche Médicale; University of Oxford","keywords":"Artificial intelligence; Machine learning; Population; Regression; Cohort; Generalization; Computer science; Psychology; Medicine; Pathology; Mathematics","authors":[{"name":"Lisa Hemforth","is_ca":false},{"name":"Baptiste Couvy‐Duchesne","is_ca":false},{"name":"Kevin de Matos","is_ca":false},{"name":"Camille Brianceau","is_ca":false},{"name":"Matthieu Joulot","is_ca":false},{"name":"Tobias Banaschewski","is_ca":false},{"name":"Arun L.W. Bokde","is_ca":false},{"name":"Sylvane Desrivières","is_ca":false},{"name":"Herta Florg","is_ca":false},{"name":"Antoine Grigis","is_ca":false},{"name":"Hugh Garavan","is_ca":false},{"name":"Penny Gowland","is_ca":false},{"name":"Andreas Heinz","is_ca":false},{"name":"Rüdiger Brühl","is_ca":false},{"name":"Jean‐Luc Martinot","is_ca":false},{"name":"Marie‐Laure Paillère Martinot","is_ca":false},{"name":"Eric Artigesn","is_ca":false},{"name":"Dimitri Papadopoulos","is_ca":false},{"name":"Herve Lemaitrei","is_ca":false},{"name":"Tomas Pausr","is_ca":true},{"name":"Luise Poustka","is_ca":false},{"name":"Sarah Hohmana","is_ca":false},{"name":"Nathalie Holz","is_ca":false},{"name":"Juliane H. Fröhner","is_ca":false},{"name":"Michael N. Smolka","is_ca":false},{"name":"Nilakshi Vaidya","is_ca":false},{"name":"Henrik Walter","is_ca":false},{"name":"Robert Whelan","is_ca":false},{"name":"Günter Schumann","is_ca":false},{"name":"Christian Büchel","is_ca":false},{"name":"JB Poline","is_ca":true},{"name":"Bernd Itterman","is_ca":false},{"name":"Vincent Frouin","is_ca":false},{"name":"Alexandre Martin","is_ca":false},{"name":"Claire Cury","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04471066165076105,"gpt":0.3432172790349323,"spread":0.2985066173841713,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006955201,0.0007853392,0.000685835,0.001330149,0.0003539653,0.0007574113,0.001044221,0.000734563,0.001359083],"category_scores_gemma":[0.01839085,0.0002360694,0.0006713501,0.0004081668,0.0003352872,0.0004828393,0.001270339,0.0007687642,0.0005739872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002505936,"about_ca_system_score_gemma":0.0003462376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003186143,"about_ca_topic_score_gemma":0.004968102,"domain_scores_codex":[0.9977666,0.0009172673,0.000185895,0.0007262786,0.0002867368,0.0001172107],"domain_scores_gemma":[0.9906695,0.00360566,0.001549178,0.001759873,0.001862078,0.0005537242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002248321,0.0002629175,0.882993,0.0001627983,0.0008690841,0.0005162779,0.0006750336,0.008002011,0.00989089,0.0003345922,0.003770761,0.09027427],"study_design_scores_gemma":[0.0001998017,0.001858318,0.8002768,0.0001046532,0.0007586624,0.002277326,0.0007981162,0.1712337,0.01736552,0.002055611,0.002918302,0.0001533734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9764221,0.0003267949,0.02046602,0.0001516128,0.00006446102,0.000104982,0.001369353,0.0003814797,0.0007132426],"genre_scores_gemma":[0.9836736,0.0001020844,0.01294743,0.00006983689,0.00003145256,0.00008578999,0.002249078,0.00005813528,0.00078268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006955201,"threshold_uncertainty_score":0.0367831,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416054896","doi":"10.59275/j.melba.2025-d1g3","title":"Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods","year":2025,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McDonnell Center for Systems Neuroscience; National Institutes of Health","keywords":"Segmentation; Metric (unit); Scale-space segmentation; Pattern recognition (psychology); Image segmentation; Field (mathematics)","authors":[{"name":"Ghazal Danaee","is_ca":false},{"name":"Marc Niethammer","is_ca":false},{"name":"Jarrett Rushmore","is_ca":false},{"name":"Sylvain Bouix","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03975856399701808,"gpt":0.3976833927445126,"spread":0.3579248287474945,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01594756,0.001119661,0.0008526068,0.001839424,0.0005252073,0.001450357,0.00126801,0.001602081,0.0009709584],"category_scores_gemma":[0.03904811,0.0003757741,0.0007495093,0.001149642,0.001234885,0.002479964,0.001390196,0.001019828,0.0004148309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300511,"about_ca_system_score_gemma":0.001541008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006387038,"about_ca_topic_score_gemma":0.007581925,"domain_scores_codex":[0.996707,0.001642341,0.0002012706,0.000641514,0.0006320472,0.0001757307],"domain_scores_gemma":[0.9805454,0.01394713,0.001644426,0.001460804,0.001892318,0.000509911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003639428,0.0005242211,0.2399285,0.001008549,0.001677017,0.0005603834,0.001718914,0.2483062,0.006368003,0.01110694,0.008927932,0.4762338],"study_design_scores_gemma":[0.00009728963,0.0006815317,0.04349007,0.0003206843,0.0002676597,0.000527599,0.0006777172,0.9282929,0.0073216,0.01390268,0.004347712,0.00007264328],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7599036,0.01312689,0.2128085,0.004711707,0.0004397541,0.0002620566,0.001087114,0.001416032,0.006244358],"genre_scores_gemma":[0.9465724,0.002296133,0.04698981,0.0007999709,0.0001740815,0.00008016174,0.001309041,0.0002831111,0.001495214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01594756,"threshold_uncertainty_score":0.08433974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400121095","doi":"10.59275/j.melba.2024-151b","title":"Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Initialization; Image registration; Artificial intelligence; Computer science; Subject (documents); Subject matter; Computer vision; Deep learning; Image (mathematics); Medical physics; Psychology; Medicine; Library science; Curriculum","authors":[{"name":"Andjela Dimitrijevic","is_ca":true},{"name":"Vincent Noblet","is_ca":false},{"name":"Benjamin De Leener","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03619910164753889,"gpt":0.3430835035040612,"spread":0.3068844018565223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008240948,0.001385069,0.0009397115,0.001203469,0.0003938683,0.001461485,0.00127863,0.00112367,0.001742502],"category_scores_gemma":[0.02378425,0.000509745,0.0008376206,0.001019247,0.0007954559,0.00146805,0.001968217,0.0009375017,0.001339089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005905269,"about_ca_system_score_gemma":0.001463428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180737,"about_ca_topic_score_gemma":0.004998941,"domain_scores_codex":[0.9960482,0.00176161,0.0004304647,0.0007698378,0.00079993,0.00019],"domain_scores_gemma":[0.9918766,0.004011853,0.0008826207,0.001891019,0.001113413,0.0002245064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005935005,0.0004179872,0.0345342,0.001238135,0.0011384,0.0006632697,0.0006742594,0.2727267,0.02290328,0.004737935,0.00853135,0.6464995],"study_design_scores_gemma":[0.0002478292,0.002550817,0.03009665,0.0003761915,0.0006592082,0.003018193,0.0005755628,0.8799958,0.06358331,0.004922561,0.01382183,0.0001520799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5201603,0.007665742,0.4496411,0.0007165186,0.0005021329,0.0004677185,0.001406115,0.01058853,0.008851795],"genre_scores_gemma":[0.7763346,0.002304868,0.210679,0.0003430844,0.00009451382,0.0002121189,0.003991132,0.002059313,0.003981384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008240948,"threshold_uncertainty_score":0.04358286,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409687339","doi":"10.59275/j.melba.2025-c1d9","title":"GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer vision; Segmentation; Artificial intelligence; Intensity (physics); Geodesic; Computer science; Image segmentation; Image (mathematics); Mathematics; Physics; Geometry; Optics","authors":[{"name":"Sukesh Adiga Vasudeva","is_ca":true},{"name":"José Dolz","is_ca":true},{"name":"Hervé Lombaert","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01298276879040531,"gpt":0.3178742906932374,"spread":0.3048915219028321,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002104556,0.001759167,0.001732607,0.003557732,0.0010474,0.002235912,0.002980286,0.002846892,0.002604258],"category_scores_gemma":[0.006678475,0.001059924,0.001686674,0.002742705,0.002356537,0.003030389,0.003745036,0.002974026,0.002157688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001532152,"about_ca_system_score_gemma":0.0021308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003750012,"about_ca_topic_score_gemma":0.006067391,"domain_scores_codex":[0.9980684,0.0004325961,0.0001103805,0.0005153153,0.0007519376,0.0001213595],"domain_scores_gemma":[0.9973435,0.0008316233,0.000426534,0.0006832783,0.0005541042,0.0001609488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004777603,0.0002215728,0.001937673,0.0004817709,0.0001513854,0.0002383807,0.0005715546,0.2493794,0.03130844,0.03149337,0.01101868,0.67272],"study_design_scores_gemma":[0.00002287861,0.0000709061,0.0003590019,0.00003281961,0.00002002667,0.0001291635,0.00005208739,0.9532621,0.01170871,0.02991495,0.004383002,0.00004447596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005190585,0.0001351288,0.9915552,0.0001498059,0.00004026422,0.00006529828,0.0001262723,0.00213826,0.0005993124],"genre_scores_gemma":[0.08740073,0.0003095568,0.9073215,0.0003200347,0.0001225392,0.0002243608,0.0009286544,0.0009698776,0.002402762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003750012,"threshold_uncertainty_score":0.01113003,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388585137","doi":"10.59275/j.melba.2023-3d9d","title":"Towards Early Prediction of Human iPSC Reprogramming Success","year":2023,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Pluripotent Stem Cells Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Induced pluripotent stem cell; Reprogramming; Computer science; Artificial intelligence; Source code; Segmentation; Regenerative medicine; Computational biology; Machine learning; Biology; Stem cell; Embryonic stem cell; Cell; Cell biology; Operating system; Genetics","authors":[{"name":"Abhineet Singh","is_ca":true},{"name":"Ila Tewari Jasra","is_ca":true},{"name":"Omar Mouhammed","is_ca":true},{"name":"Nidheesh Dadheech","is_ca":true},{"name":"Nilanjan Ray","is_ca":true},{"name":"A. M. James Shapiro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01815780357043608,"gpt":0.3210128194884764,"spread":0.3028550159180403,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002164911,0.001011739,0.0006334828,0.002949289,0.0002272274,0.001404244,0.0005371309,0.001118722,0.002370748],"category_scores_gemma":[0.01204457,0.0003118811,0.0005783304,0.0009624027,0.0002956238,0.0009041521,0.0008286944,0.0012092,0.003128096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000481161,"about_ca_system_score_gemma":0.0004663226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003100893,"about_ca_topic_score_gemma":0.003149913,"domain_scores_codex":[0.9990336,0.0001716138,0.0001037694,0.000316147,0.00028866,0.00008632665],"domain_scores_gemma":[0.992523,0.00491063,0.0006673906,0.0004558681,0.001187796,0.0002552183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001049827,0.00025246,0.3288798,0.0006066067,0.0002457463,0.0005483617,0.0002809498,0.1392704,0.01902932,0.002521266,0.03229228,0.4750229],"study_design_scores_gemma":[0.0000407472,0.0002500571,0.1006726,0.0002301943,0.0001121265,0.0006612692,0.0001271194,0.8495573,0.02626777,0.006894028,0.01508926,0.00009752862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5223091,0.007749963,0.4037273,0.001175945,0.0004427255,0.000303408,0.03991657,0.01015632,0.01421877],"genre_scores_gemma":[0.8790195,0.002085038,0.08448937,0.0003827364,0.0002150842,0.0003041089,0.02915308,0.0005425139,0.003808516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003100893,"threshold_uncertainty_score":0.01144928,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7124924134","doi":"10.59275/j.melba.2025-fcb7","title":"A schistosomiasis dataset with bright- and darkfield images","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Toronto General Hospital; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Schistosomiasis; Freshwater mollusc; Helminthiasis; Biomphalaria","authors":[{"name":"Dieudonné Kigbafori Silue","is_ca":false},{"name":"María Díaz de León Derby","is_ca":false},{"name":"Charles B. Delahunt","is_ca":false},{"name":"Anne-Laure M. Le Ny","is_ca":false},{"name":"Ethan Spencer","is_ca":false},{"name":"Maxim Armstrong","is_ca":false},{"name":"Karla Fisher","is_ca":true},{"name":"Daniel A. Fletcher","is_ca":false},{"name":"Isaac I. Bogoch","is_ca":true},{"name":"Jean T. Coulibaly","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005062693270219134,"gpt":0.2550440449348918,"spread":0.2499813516646727,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006344014,0.001664452,0.001138683,0.00266119,0.0007598284,0.001019795,0.001957602,0.001952345,0.006415212],"category_scores_gemma":[0.002208642,0.0004776405,0.001551869,0.002152495,0.000529847,0.0006175778,0.001459794,0.00117182,0.005411653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156098,"about_ca_system_score_gemma":0.001071812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02825774,"about_ca_topic_score_gemma":0.05947569,"domain_scores_codex":[0.9992893,0.0001096958,0.00009999411,0.0001768292,0.0001942128,0.0001301031],"domain_scores_gemma":[0.9993479,0.0001432688,0.00006435117,0.0001642851,0.0001754348,0.0001047915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001751935,0.000933486,0.03126541,0.004596435,0.0008077607,0.002866802,0.0002035066,0.00995106,0.01433039,0.0009152229,0.8470647,0.08531331],"study_design_scores_gemma":[0.001711703,0.0009852059,0.2439409,0.001372918,0.000499677,0.01002575,0.001063863,0.06610277,0.01395546,0.002481827,0.6574746,0.0003852061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0525756,0.004151873,0.003187143,0.001515433,0.0004329197,0.0006844227,0.9285502,0.00542911,0.003473342],"genre_scores_gemma":[0.03387075,0.0006096599,0.005752075,0.0002029414,0.00005799935,0.0002236909,0.9582489,0.0000755318,0.0009584955],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02825774,"threshold_uncertainty_score":0.0561865,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417539435","doi":"10.59275/j.melba.2025-6838","title":"Exploring Fairness and Performance Drivers Across State-of-the-Art Pulmonary Nodule Detection Algorithms","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Medical Research Council; Stand Up To Cancer; CRUK Lung Cancer Centre of Excellence; UK Regenerative Medicine Platform; University College London; National Institute for Health and Care Research; Cancer Research UK; LUNGevity Foundation; Wellcome Trust; F. Hoffmann-La Roche; Microsoft Research; University College London Hospitals NHS Foundation Trust; Rosetrees Trust; Roy Castle Lung Cancer Foundation; Gilead Sciences; American Association for Cancer Research; AstraZeneca; GlaxoSmithKline","keywords":"Lung cancer screening; Nodule (geology); Discriminative model; National Lung Screening Trial; Lung cancer; Asymptomatic; Computed tomography; Cancer detection","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.01740001303978086,"gpt":0.2909937764071602,"spread":0.2735937633673793,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02993584,0.000912602,0.0009688677,0.001554787,0.0009537834,0.003669117,0.001708037,0.001815519,0.001650265],"category_scores_gemma":[0.1084142,0.0003613073,0.0006411197,0.001067089,0.002076579,0.005244105,0.002220329,0.002072573,0.0009800961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001962721,"about_ca_system_score_gemma":0.001600794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00552257,"about_ca_topic_score_gemma":0.003840826,"domain_scores_codex":[0.9882396,0.005491293,0.0008559043,0.002484899,0.002190386,0.0007379532],"domain_scores_gemma":[0.9206562,0.06206366,0.002957349,0.006304215,0.006800554,0.001217927],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01000292,0.00137098,0.2655244,0.001033421,0.0008315464,0.0002806696,0.001377139,0.2724969,0.007976322,0.01938016,0.01590401,0.4038216],"study_design_scores_gemma":[0.000448071,0.002536812,0.05619235,0.0002720504,0.0002345275,0.0003404917,0.0008069766,0.8832791,0.01125184,0.03776649,0.006764438,0.0001067847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9308383,0.009876371,0.0398933,0.005342944,0.0004899258,0.0001843362,0.001104729,0.0008763826,0.01139376],"genre_scores_gemma":[0.9864884,0.0006180634,0.009818821,0.0004990326,0.0002105554,0.00006608174,0.001115572,0.0001401574,0.001043389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9700642,"threshold_uncertainty_score":0.1583178,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7124836711","doi":"10.59275/j.melba.2025-f593","title":"SurgiSR4K: A High‑Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Usability; CLARITY; Invasive surgery; Visualization; Monocular; Minimally invasive procedures","authors":[{"name":"Fengyi Jiang","is_ca":false},{"name":"Lingbo Jin","is_ca":false},{"name":"Ruixing Liang","is_ca":false},{"name":"Yuxin Chen","is_ca":true},{"name":"Adi Chola Venkatesh","is_ca":false},{"name":"Jason Culman","is_ca":false},{"name":"Lirong Shao","is_ca":false},{"name":"Wenqing Sun","is_ca":false},{"name":"Cong Gao","is_ca":false},{"name":"Hallie McNamara","is_ca":false},{"name":"Jingpei Lu","is_ca":false},{"name":"Omid Mohareri","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02089766755471499,"gpt":0.3272262943528538,"spread":0.3063286267981388,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006779811,0.002200149,0.0009696434,0.002231276,0.0005547268,0.001286282,0.002095597,0.002008818,0.00797426],"category_scores_gemma":[0.0028656,0.0005258908,0.001628331,0.001824825,0.0005467742,0.000891339,0.001866507,0.001479939,0.00995434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008557074,"about_ca_system_score_gemma":0.001523347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01116021,"about_ca_topic_score_gemma":0.02523335,"domain_scores_codex":[0.9989356,0.0001243214,0.0001129012,0.0003003781,0.000372033,0.0001547134],"domain_scores_gemma":[0.9991693,0.0001566209,0.0001115513,0.0002281957,0.0002366727,0.00009759917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001732583,0.0007332003,0.01698683,0.00470286,0.0006531448,0.001622827,0.0003439685,0.02150921,0.03391132,0.002284605,0.7297523,0.1857671],"study_design_scores_gemma":[0.0006786996,0.0009289245,0.09972185,0.001642725,0.0004432281,0.008183936,0.0009731123,0.09700843,0.05212574,0.007941391,0.729705,0.0006470924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06335927,0.004726626,0.03784665,0.0008568392,0.0007762865,0.0007916933,0.8594017,0.02063746,0.01160347],"genre_scores_gemma":[0.04502505,0.0008983043,0.02708449,0.0002283173,0.00007340134,0.0004877414,0.9226985,0.0008896666,0.002614559],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01116021,"threshold_uncertainty_score":0.0266766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7124958874","doi":"10.59275/j.melba.2025-5ce1","title":"The Trauma THOMPSON Dataset for Real-World Emergency AI","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"U.S. Army Medical Research and Development Command; National Science Foundation","keywords":"Action (physics); Benchmark (surveying); Object (grammar); Psychological intervention; Emergency response; Foundation (evidence); Visualization","authors":[{"name":"Yupeng Zhuo","is_ca":false},{"name":"Eddie Zhang","is_ca":false},{"name":"Xiangchen Yu","is_ca":false},{"name":"Aditya Pachpande","is_ca":false},{"name":"Kyle Couperus","is_ca":false},{"name":"Jessica McKee","is_ca":true},{"name":"Juan Wachs","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01384478357073097,"gpt":0.3552164647173537,"spread":0.3413716811466227,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016855,0.003604635,0.001446933,0.003269707,0.001152561,0.002047667,0.004456006,0.002708186,0.01804244],"category_scores_gemma":[0.005568417,0.0004631628,0.001845455,0.003678374,0.0006269795,0.001924868,0.002401802,0.002431554,0.02149935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002173109,"about_ca_system_score_gemma":0.001940977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03718197,"about_ca_topic_score_gemma":0.086002,"domain_scores_codex":[0.9982431,0.0003160756,0.0002226575,0.0004472449,0.0005858355,0.000185153],"domain_scores_gemma":[0.9983258,0.0005014327,0.0001136703,0.0004100738,0.0004453576,0.0002037688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000171117,0.0002669713,0.00172315,0.00107049,0.000106806,0.0002114517,0.00007188642,0.00470097,0.0007148526,0.0008779138,0.9635647,0.02651975],"study_design_scores_gemma":[0.0004957896,0.0002804644,0.01407629,0.0009594468,0.0001712699,0.001187685,0.0007680362,0.07255171,0.005057757,0.007323959,0.8969161,0.0002114065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01378542,0.003484595,0.006212142,0.001908509,0.001075854,0.0007107786,0.9409431,0.01364237,0.0182373],"genre_scores_gemma":[0.009135186,0.0003680872,0.006589737,0.0003265432,0.00006857302,0.0003016652,0.981065,0.0002258114,0.001919298],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03718197,"threshold_uncertainty_score":0.0739311,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393905672","doi":"10.59275/j.melba.2024-267f","title":"Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning","year":2024,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"MacEwan University; University of Alberta","funders":"Canadian Institutes of Health Research; Women and Children's Health Research Institute; Canada Research Chairs; Children's Health Research Institute","keywords":"Autoencoder; Hippocampal formation; Neuroscience; Psychology; Graph; Artificial intelligence; Pattern recognition (psychology); Medicine; Computer science; Deep learning; Theoretical computer science","authors":[{"name":"Jakaria Rabbi","is_ca":true},{"name":"Johannes Kiechle","is_ca":false},{"name":"Christian Beaulieu","is_ca":true},{"name":"Nilanjan Ray","is_ca":true},{"name":"Dana Cobzaş","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03634657959718149,"gpt":0.3565907821513885,"spread":0.320244202554207,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133851,0.0006791008,0.0003970552,0.0007803545,0.0001557609,0.0004270265,0.0005183507,0.0006135803,0.0004604821],"category_scores_gemma":[0.003830975,0.0002764144,0.0008881725,0.00038673,0.0006745614,0.0007533259,0.0006590192,0.001004788,0.0001220903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003630428,"about_ca_system_score_gemma":0.0003557747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006017677,"about_ca_topic_score_gemma":0.006759906,"domain_scores_codex":[0.9997351,0.00009144611,0.0000142681,0.0000946697,0.0000384161,0.00002611364],"domain_scores_gemma":[0.9988778,0.0007641462,0.000105254,0.0001183327,0.0000910674,0.00004335838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005149744,0.0001491297,0.05288216,0.000200022,0.0006707661,0.0005935819,0.0004335347,0.6923649,0.02232571,0.007388166,0.002187667,0.2202893],"study_design_scores_gemma":[0.000006065955,0.00004610207,0.005811503,0.00001416083,0.00003018602,0.0001097166,0.00003578755,0.9877079,0.002196783,0.003524287,0.0005059034,0.00001166975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4541044,0.002683274,0.5402535,0.0007616436,0.00007397104,0.00004559279,0.0004506444,0.0003609665,0.001266106],"genre_scores_gemma":[0.9424269,0.0006140947,0.0544138,0.0001314958,0.0000558281,0.00002631907,0.0007638328,0.00007875667,0.001488965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006017677,"threshold_uncertainty_score":0.01196533,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}