{"id":"W3006530269","doi":"10.1007/s12021-019-09439-6","title":"Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Centre For Medical Engineering, King’s College London; Servier; Eisai; University of Southern California; Wellcome Trust; University College London; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Alzheimer's Society; 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; Wolfson Foundation; Brain Research Trust; Pennington Biomedical Research Foundation; Foundation for the National Institutes of Health","keywords":"Hyperintensity; Artificial intelligence; Bayesian probability; Computer science; Segmentation; Cognition; Feature selection; Selection (genetic algorithm); Pattern recognition (psychology); Machine learning; Psychology; Medicine; Magnetic resonance imaging; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.00006758476,0.0001257131,0.0001245195,0.00006944536,0.0001507542,0.0001819233,0.0001157931,0.00003530642,0.00002118999],"category_scores_gemma":[0.00001549863,0.0001136188,0.0000158464,0.0003160786,0.0000481073,0.001558118,0.0001043872,0.0001621538,0.00001181811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003957742,"about_ca_system_score_gemma":0.00006311526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004074736,"about_ca_topic_score_gemma":0.000001074044,"domain_scores_codex":[0.9991115,0.00003652724,0.0002535876,0.0001631597,0.0002880863,0.0001471025],"domain_scores_gemma":[0.9994139,0.00003347995,0.0001532372,0.0001002746,0.0001538939,0.0001451852],"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":[0.0002193988,0.0006472879,0.663611,0.001999592,0.0003572394,0.000160263,0.1565302,0.0528908,0.01466359,0.002603576,0.02023143,0.0860856],"study_design_scores_gemma":[0.0003006663,0.0001190891,0.01669526,0.00002820119,0.00001971642,0.0000753217,0.0002112235,0.9817212,0.0006883857,0.0000178623,0.00000199041,0.0001210632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02808624,0.000001298779,0.9693244,0.001084692,0.00003218933,0.0004304779,0.000006181822,0.0005978774,0.0004366121],"genre_scores_gemma":[0.4418715,0.00000321125,0.5505298,0.007534125,0.0000150771,0.00001767424,0.00001210552,0.000008207518,0.000008313203],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9288304,"threshold_uncertainty_score":0.4633241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0552192469157412,"score_gpt":0.3116836159447391,"score_spread":0.2564643690289979,"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."}}