{"id":"W4388806341","doi":"10.1016/j.media.2023.103041","title":"WarpDrive: Improving spatial normalization using manual refinements","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Toronto Rehabilitation Institute; McGill University; Douglas Mental Health University Institute; Krembil Foundation; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Eisai; Deutsche Forschungsgemeinschaft; Servier; EU Joint Programme – Neurodegenerative Disease Research; Northern California Institute for Research and Education; National Institute of Mental Health; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Artificial intelligence; Spatial normalization; Normalization (sociology); Inference; Modalities; Pattern recognition (psychology); Neuroimaging; Process (computing); Computer vision; Machine learning; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002539447,0.0001141491,0.0002659805,0.0004154349,0.0001305747,0.00002035878,0.0001195433,0.00006718849,0.00068508],"category_scores_gemma":[0.0003420479,0.0001013072,0.0001670641,0.002056781,0.00008777613,0.00009295217,0.0001295536,0.0001777948,0.00006608899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004748712,"about_ca_system_score_gemma":0.00005275671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003276153,"about_ca_topic_score_gemma":0.00002459988,"domain_scores_codex":[0.9985041,0.00002789376,0.0003089141,0.0003167778,0.0005925278,0.0002498071],"domain_scores_gemma":[0.9992216,0.00003541832,0.00009096065,0.0003468023,0.0001065282,0.0001986813],"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":[0.0001668328,0.001485347,0.2793857,0.0004796483,0.002645926,0.002504259,0.0005227127,0.001076294,0.2179523,0.0007254654,0.01660411,0.4764515],"study_design_scores_gemma":[0.0006417712,0.00006686795,0.03466105,0.00004279233,0.002127551,0.00002816777,0.00005256164,0.9515358,0.006218005,0.0001691265,0.004249609,0.0002067144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1598126,0.00001180904,0.8369536,0.002141397,0.00002929707,0.0001574316,0.000009897863,0.0004415887,0.0004423991],"genre_scores_gemma":[0.9651139,0.000102565,0.03184136,0.001191049,0.0002600744,0.0000409015,0.0005360024,0.00003527239,0.0008788353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9504595,"threshold_uncertainty_score":0.7501145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05912185879799546,"score_gpt":0.4208470339866595,"score_spread":0.361725175188664,"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."}}