{"id":"W4405591760","doi":"10.1002/hbm.70082","title":"Subject‐Level Segmentation Precision Weights for Volumetric Studies Involving Label Fusion","year":2024,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; Northern California Institute for Research and Education; National Institute of Mental Health; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Segmentation; Weighting; Artificial intelligence; Computer science; Pattern recognition (psychology); Neuroimaging; Region of interest; Market segmentation; Volume (thermodynamics); Psychology; Medicine; Neuroscience; Radiology","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":[0.01488188,0.00140212,0.001458817,0.002886474,0.0009748608,0.001846097,0.001997153,0.002566922,0.0017899],"category_scores_gemma":[0.04618623,0.0009052197,0.001261433,0.003338735,0.001439364,0.002263397,0.002237929,0.001882257,0.0006314917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132443,"about_ca_system_score_gemma":0.001544814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001870899,"about_ca_topic_score_gemma":0.002934434,"domain_scores_codex":[0.9950367,0.001903908,0.0004552276,0.001195984,0.001249076,0.000159074],"domain_scores_gemma":[0.9856318,0.008008958,0.001738688,0.002675779,0.001739017,0.0002056444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001221699,0.0003248735,0.01263961,0.0008473165,0.001169464,0.00019011,0.0008216669,0.08696868,0.07243671,0.03473834,0.004435763,0.7842057],"study_design_scores_gemma":[0.0002224708,0.0007160163,0.0308195,0.0003317839,0.0007483404,0.0007624316,0.0002058943,0.7696969,0.07156933,0.1092482,0.01545133,0.0002277244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01436198,0.0007748299,0.9832941,0.0001329936,0.00006649145,0.0001247462,0.00009655369,0.0006729002,0.0004752811],"genre_scores_gemma":[0.1709786,0.0005711012,0.8256145,0.000207518,0.0001738304,0.0004063463,0.0004595058,0.0006248003,0.0009637604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01488188,"threshold_uncertainty_score":0.07870382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1399858512237404,"score_gpt":0.3808594240639258,"score_spread":0.2408735728401853,"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."}}