{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006658932,0.002854286,0.001678817,0.00362121,0.0008106499,0.002824925,0.003301589,0.001570489,0.009529329],"category_scores_gemma":[0.03445955,0.001645075,0.002041362,0.002488262,0.001197343,0.002541522,0.004366526,0.002573184,0.005153246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006001685,"about_ca_system_score_gemma":0.001827759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004958828,"about_ca_topic_score_gemma":0.009891706,"domain_scores_codex":[0.9957487,0.001009851,0.0004581749,0.0009707272,0.001595138,0.0002174548],"domain_scores_gemma":[0.9914402,0.004487018,0.0008340187,0.00196433,0.001138382,0.000136057],"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":[0.0006586176,0.0002095448,0.005161286,0.0007345771,0.0005682037,0.000511133,0.001143426,0.03362662,0.04207724,0.009211241,0.04052108,0.865577],"study_design_scores_gemma":[0.0002718932,0.0003858483,0.007889916,0.0002750009,0.0002647158,0.002170211,0.0006706959,0.6831782,0.1479402,0.03621325,0.1203417,0.0003982715],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005934468,0.0002744725,0.9689075,0.0001283868,0.0001101036,0.0001046752,0.0005243781,0.02318606,0.0008300665],"genre_scores_gemma":[0.0520213,0.0003017458,0.9348158,0.0001988574,0.00005295119,0.0003127911,0.001954277,0.008566546,0.001775753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009529329,"threshold_uncertainty_score":0.03521621,"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."}}