{"id":"W4221129115","doi":"10.1002/hbm.25784","title":"Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; Toronto Rehabilitation Institute; Ontario Brain Institute; Université de Montréal; Heart and Stroke Foundation; York University; Montreal Heart Institute; Toronto Western Hospital; University Health Network; Ottawa Hospital; Thunder Bay Regional Research Institute; University of Ottawa; Sunnybrook Health Science Centre; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Western University; University of Toronto","funders":"Faculty of Health Sciences, Queen's University; London Health Sciences Foundation; Government of Ontario; St. Michael's Hospital Foundation; University Health Network; Temerty Family Foundation; Health Sciences Centre Foundation; Ontario Brain Institute; University of Ottawa; Queen's University; Canadian Institutes of Health Research; Centre for Addiction and Mental Health Foundation; McMaster University","keywords":"Hyperintensity; Segmentation; Artificial intelligence; Adversarial system; White matter; Bayesian probability; Pattern recognition (psychology); Computer science; Psychology; Magnetic resonance imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002851749,0.001370301,0.001197598,0.001006856,0.0004884795,0.001083179,0.001716194,0.001953579,0.001822583],"category_scores_gemma":[0.008186032,0.001037743,0.001167115,0.000502323,0.001287544,0.001198539,0.001959313,0.002842549,0.0004199078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002199945,"about_ca_system_score_gemma":0.001551737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01857163,"about_ca_topic_score_gemma":0.01463483,"domain_scores_codex":[0.9991154,0.0003569471,0.00004784535,0.0002111874,0.0001637859,0.0001047989],"domain_scores_gemma":[0.9966102,0.002396199,0.0003118259,0.0001618454,0.0004091685,0.0001106946],"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":[0.0000549984,0.00001373454,0.0003764616,0.00002042276,0.00002588913,0.00003081467,0.00002572188,0.980262,0.00052484,0.003220198,0.0003224132,0.01512257],"study_design_scores_gemma":[0.000001505804,0.000004913857,0.0000410301,0.000003721927,0.000002096325,0.000004753136,0.000001170985,0.9982433,0.0001759066,0.001446648,0.00007248155,0.00000243514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02984955,0.0006077723,0.9663413,0.0004714395,0.00005455586,0.00006729069,0.0001811128,0.0007925202,0.001634456],"genre_scores_gemma":[0.8153161,0.0005100134,0.1776111,0.0004529488,0.00009007381,0.0002680364,0.0005901028,0.0002282671,0.004933264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01857163,"threshold_uncertainty_score":0.0369271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137712265731811,"score_gpt":0.2520364986494728,"score_spread":0.2382652720762916,"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."}}