{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387122,0.0001845783,0.0002078492,0.0007772945,0.0004977608,0.0004451971,0.000504069,0.00007211851,0.00003095053],"category_scores_gemma":[0.0006288463,0.0001676924,0.00007994964,0.001034928,0.00005984996,0.001017671,0.0003206585,0.0001545951,0.00003429008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002207027,"about_ca_system_score_gemma":0.00005009966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001132954,"about_ca_topic_score_gemma":0.000007393875,"domain_scores_codex":[0.9980375,0.0001285079,0.000453767,0.0005902561,0.00049001,0.00029992],"domain_scores_gemma":[0.998246,0.001056418,0.000108522,0.0003232663,0.0001830894,0.00008268111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003672555,0.00005524907,0.00008458462,0.000582828,0.00007160326,0.00001765008,0.007792168,0.000001769687,0.5458362,0.01993549,0.05592175,0.3696971],"study_design_scores_gemma":[0.003943192,0.001621854,0.01595799,0.006892971,0.0001072569,0.00005591419,0.004631695,0.3605355,0.2045802,0.379221,0.01986934,0.002583123],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01411559,0.002463465,0.9801617,0.000916462,0.0005966066,0.0007259648,0.000004295171,0.0008741066,0.0001418099],"genre_scores_gemma":[0.07010586,0.0002265567,0.9237483,0.001630037,0.0004215996,0.0003790917,0.00005577831,0.00004521669,0.003387604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.367114,"threshold_uncertainty_score":0.6838298,"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."}}