{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009175428,0.00005342168,0.00007709409,0.00004132052,0.001431847,0.00002205547,0.0007294866,0.00006010083,0.0001861372],"category_scores_gemma":[0.0009309822,0.00003791621,0.00005388907,0.0002404596,0.0001525627,0.00002215489,0.0004811985,0.002416166,0.00001034455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009320196,"about_ca_system_score_gemma":0.000159159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001611205,"about_ca_topic_score_gemma":0.00002847774,"domain_scores_codex":[0.9988829,0.0002007354,0.0001566099,0.00009450985,0.000543966,0.0001212125],"domain_scores_gemma":[0.9983391,0.0005156055,0.00005638499,0.0009355106,0.00005057231,0.0001028846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001329998,0.0008315836,0.04002739,0.00002336275,0.0002560079,0.00005721162,0.001436334,0.0000804704,0.002485523,0.1557404,0.2502589,0.5486698],"study_design_scores_gemma":[0.0004700451,0.00004440815,0.00780687,0.000009158167,0.00003363989,0.0002355703,0.0004071413,0.01736996,0.00003011753,0.0003496414,0.97319,0.00005342435],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07596477,0.03738828,0.001149642,0.8407227,0.001310721,0.0007846337,0.00001095011,0.00028236,0.04238589],"genre_scores_gemma":[0.9868758,0.001220956,0.003444534,0.007368573,0.00007644531,0.0001163223,0.0001335644,0.00001315703,0.0007506417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.910911,"threshold_uncertainty_score":0.9998853,"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."}}