{"id":"W4378771036","doi":"10.48550/arxiv.2305.18033","title":"The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Orionin Tutkimussäätiö; Turun Yliopisto; Vetenskapsrådet; Cancerfonden; VINNOVA; Karolinska Institutet; European Commission; Swedish e-Science Research Centre; Turun Yliopistosäätiö; Syöpäsäätiö; European Federation of Pharmaceutical Industries and Associations","keywords":"Computer science; Image registration; Artificial intelligence; Breast cancer; Cancer; Medicine; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009729514,0.002436594,0.001990331,0.002517308,0.001559493,0.004072618,0.003163304,0.003622756,0.004691879],"category_scores_gemma":[0.0216905,0.0008793293,0.002601592,0.002488345,0.001512419,0.002236998,0.005198333,0.003212226,0.009168817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000025,"about_ca_system_score_gemma":0.002734236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004934573,"about_ca_topic_score_gemma":0.007986189,"domain_scores_codex":[0.9923317,0.001848335,0.000646755,0.002463365,0.002184804,0.0005250219],"domain_scores_gemma":[0.9917461,0.002375324,0.0006149372,0.003487174,0.001317965,0.0004584855],"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":[0.002402949,0.0007819191,0.01383153,0.002703114,0.001172917,0.0008677424,0.0005659286,0.0270575,0.07776648,0.007262466,0.4256487,0.4399388],"study_design_scores_gemma":[0.0009535452,0.001586918,0.04280906,0.0006050403,0.0005048682,0.008398992,0.001140149,0.3617523,0.1491425,0.02789401,0.4047008,0.0005117851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2585537,0.02098916,0.487656,0.009968261,0.009421748,0.002653773,0.09335121,0.08983218,0.02757392],"genre_scores_gemma":[0.283015,0.003183252,0.4368262,0.002642894,0.00114755,0.001526673,0.2436309,0.01090217,0.01712536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009729514,"threshold_uncertainty_score":0.05145526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0761882580260853,"score_gpt":0.2223252595551805,"score_spread":0.1461370015290951,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}