{"id":"W3172681723","doi":"10.1038/s41467-022-30695-9","title":"The Medical Segmentation Decathlon","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1202,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Polytechnique Montréal","funders":"National Institute of Neurological Disorders and Stroke; NIH Clinical Center; National Cancer Institute; National Institutes of Health; Engineering and Physical Sciences Research Council; KWF Kankerbestrijding; National Institute for Health and Care Research; Siemens Healthineers; UK Research and Innovation; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust","keywords":"Generalizability theory; Segmentation; Computer science; Task (project management); Set (abstract data type); Artificial intelligence; Machine learning; Image segmentation; Modalities; Image (mathematics); Range (aeronautics); Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01543938,0.001810761,0.001642812,0.002867306,0.002114765,0.004613854,0.00296456,0.004849663,0.008825497],"category_scores_gemma":[0.03097953,0.0009734366,0.00198105,0.001792371,0.003101856,0.004964124,0.009047545,0.005454117,0.004600421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002966353,"about_ca_system_score_gemma":0.0041099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525652,"about_ca_topic_score_gemma":0.003694311,"domain_scores_codex":[0.9904572,0.002845687,0.0005782885,0.002878137,0.002678927,0.0005616697],"domain_scores_gemma":[0.9721707,0.0103862,0.001345134,0.005809908,0.007178943,0.003109173],"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":[0.002311042,0.0007389994,0.00845598,0.002492011,0.0005546083,0.0005489714,0.001374746,0.03135239,0.02057909,0.05292686,0.3007253,0.57794],"study_design_scores_gemma":[0.0004917932,0.003670773,0.0262322,0.00120468,0.0002263055,0.00381632,0.002089753,0.1803984,0.06012047,0.1516159,0.5696478,0.0004856763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2192031,0.05206824,0.5154176,0.07111052,0.02436211,0.004161825,0.02457582,0.007849262,0.08125145],"genre_scores_gemma":[0.4591287,0.007631741,0.4284997,0.01253564,0.004865843,0.002629245,0.03260712,0.003532007,0.04857009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01543938,"threshold_uncertainty_score":0.08165222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289525745999718,"score_gpt":0.355635835647866,"score_spread":0.3427405781878688,"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."}}