{"id":"W4313551754","doi":"10.47936/encephalitis.2022.00108","title":"Improving performance robustness of subject-based brain segmentation software","year":2023,"lang":"en","type":"article","venue":"encephalitis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Segmentation; Artificial intelligence; Computer science; Preprocessor; Data pre-processing; Generalizability theory; Pattern recognition (psychology); Robustness (evolution); Software; Statistics; Mathematics","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":[0.004839782,0.001311251,0.0009245903,0.00193534,0.0004203009,0.001456891,0.001130691,0.0009061637,0.001432372],"category_scores_gemma":[0.01467324,0.0004246797,0.001135225,0.001005327,0.0004289862,0.000932166,0.001116807,0.0007397432,0.000966077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007879253,"about_ca_system_score_gemma":0.00112357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004321601,"about_ca_topic_score_gemma":0.002901541,"domain_scores_codex":[0.9974497,0.0007387761,0.0001891563,0.0009272834,0.00055374,0.0001412595],"domain_scores_gemma":[0.9957585,0.001964302,0.0003446134,0.0007053691,0.001120172,0.0001070955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002609147,0.0004403686,0.05021031,0.0004063473,0.001446047,0.0003423594,0.0006071922,0.1541246,0.1167721,0.001162326,0.005371509,0.6665078],"study_design_scores_gemma":[0.00006174749,0.0006319334,0.03154242,0.000033491,0.0002181016,0.0003434965,0.00009877595,0.8990619,0.06351827,0.001138419,0.003279169,0.00007231332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6309043,0.001694489,0.3421236,0.0003524938,0.0002983143,0.0002319425,0.000659348,0.02101441,0.002721102],"genre_scores_gemma":[0.8855278,0.0003081997,0.109711,0.0001671898,0.00008404782,0.0001313329,0.00199546,0.0008600104,0.001215065],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004839782,"threshold_uncertainty_score":0.02559549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03347413287791966,"score_gpt":0.2673524906866486,"score_spread":0.233878357808729,"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."}}