{"id":"W3213990473","doi":"10.1016/j.media.2023.103058","title":"Acquisition-invariant brain MRI segmentation with informative uncertainties","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"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; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; Wellcome Trust; University of Southern California; Engineering and Physical Sciences Research Council; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; University College London Hospitals NHS Foundation Trust; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Segmentation; Computer science; Task (project management); Artificial intelligence; Context (archaeology); Machine learning; Invariant (physics); Quality (philosophy); Data mining; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001087195,0.0001671585,0.000460093,0.0006685549,0.0001573641,0.00007296744,0.0001368878,0.00008698866,0.002263166],"category_scores_gemma":[0.0009044532,0.0001143614,0.0001976978,0.002724417,0.0003068321,0.00021727,0.00006322149,0.0004159655,0.0002529173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006917728,"about_ca_system_score_gemma":0.0001686827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000213497,"about_ca_topic_score_gemma":0.00002431779,"domain_scores_codex":[0.9977127,0.00009855854,0.0003836516,0.0002682546,0.001194313,0.0003425251],"domain_scores_gemma":[0.9987376,0.0003166434,0.0001173567,0.0002652679,0.0001539926,0.0004091143],"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":[0.001379034,0.001106921,0.2696352,0.001398184,0.03331619,0.01239558,0.03062177,0.01127748,0.009199643,0.005034866,0.4494113,0.1752238],"study_design_scores_gemma":[0.003531238,0.0003516897,0.06587263,0.000262431,0.003490137,0.0001371104,0.004921779,0.9140431,0.0007574196,0.0003971172,0.005816384,0.0004189872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2436808,0.0000798546,0.6314467,0.1161589,0.000117859,0.0003916488,0.000015853,0.0005690511,0.007539369],"genre_scores_gemma":[0.9552686,0.0003078837,0.01617168,0.02066483,0.0004638136,0.00008958968,0.001941707,0.00005261145,0.005039284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9027656,"threshold_uncertainty_score":0.9986489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00644885826553453,"score_gpt":0.2968551385485205,"score_spread":0.2904062802829859,"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."}}