{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01515046,0.001202869,0.0008940056,0.002258319,0.0003770444,0.001041416,0.0006327412,0.0008126589,0.0006444152],"category_scores_gemma":[0.03390102,0.0004363577,0.0009560884,0.001062856,0.000587331,0.001056372,0.0009992312,0.0007160368,0.0001827769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006442266,"about_ca_system_score_gemma":0.0008372555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008412994,"about_ca_topic_score_gemma":0.00946683,"domain_scores_codex":[0.9961526,0.002537132,0.0002246942,0.0005239313,0.0004334192,0.0001282149],"domain_scores_gemma":[0.9814926,0.01319118,0.002095073,0.001161025,0.001761207,0.0002989185],"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":[0.002644635,0.0003537428,0.7270855,0.0002471271,0.002165428,0.0001400419,0.000923534,0.1617572,0.005384629,0.00136443,0.001281481,0.09665234],"study_design_scores_gemma":[0.0001020837,0.0007268225,0.1908739,0.00005950378,0.0002680472,0.0001668901,0.0001739444,0.8007545,0.00235156,0.003838718,0.0005978149,0.00008612168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9028283,0.0005146561,0.09472017,0.0001666762,0.00001653839,0.0001986177,0.000483412,0.0004826096,0.0005890469],"genre_scores_gemma":[0.964959,0.0001063318,0.03371248,0.00003183092,0.00001560432,0.0001626085,0.0007507636,0.00005425498,0.0002070173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01515046,"threshold_uncertainty_score":0.08012426,"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."}}