{"id":"W3196049305","doi":"10.1101/2021.08.18.456666","title":"Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; 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; Toronto Rehabilitation Institute; 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; Canadian Institutes of Health Research; London Health Sciences Foundation; Temerty Family Foundation; Health Sciences Centre Foundation; University of Ottawa; Ontario Brain Institute; Government of Ontario; Queen's University; Centre for Addiction and Mental Health Foundation; McMaster University","keywords":"Segmentation; Artificial intelligence; Computer science; Robustness (evolution); Pattern recognition (psychology); Convolutional neural network; Neuroimaging; Bayesian probability; Hyperintensity; Deep learning; Hausdorff distance; Pipeline (software); Machine learning; Magnetic resonance imaging; Medicine","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":[0.003115015,0.001373011,0.001150501,0.001033222,0.0004542992,0.001034119,0.001613064,0.002014193,0.001805666],"category_scores_gemma":[0.00815462,0.0009639041,0.001096291,0.0004370358,0.001298906,0.001149888,0.001871503,0.002637824,0.0003835749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002211591,"about_ca_system_score_gemma":0.001364925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01649747,"about_ca_topic_score_gemma":0.01222677,"domain_scores_codex":[0.9991559,0.0003530965,0.00004268947,0.0001949834,0.0001515419,0.0001019294],"domain_scores_gemma":[0.9961241,0.002786101,0.0003520244,0.0001750551,0.0004422076,0.0001205292],"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":[0.00005632396,0.00001399958,0.0003799387,0.00001976437,0.00002549028,0.00003144581,0.00002083271,0.9829185,0.0005117707,0.002411234,0.0002790091,0.01333177],"study_design_scores_gemma":[0.000001206739,0.000004661664,0.00003543153,0.000003246929,0.000001593303,0.000003859591,8.755709e-7,0.9987776,0.000151071,0.0009715614,0.00004706476,0.000001795794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04788539,0.0007583837,0.9476296,0.0006279456,0.00007000555,0.00007833552,0.0001986154,0.0008750472,0.00187664],"genre_scores_gemma":[0.8700943,0.0003739265,0.1244648,0.000376401,0.00007792484,0.0001704091,0.0004438741,0.0001853761,0.003813069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01649747,"threshold_uncertainty_score":0.03280288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972689814348426,"score_gpt":0.2693101865244165,"score_spread":0.2495832883809323,"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."}}