{"id":"W4226199676","doi":"10.5167/uzh-214495","title":"Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge","year":2021,"lang":"en","type":"article","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Generalizability theory; Benchmarking; Segmentation; Computer science; Deep learning; Vendor; Artificial intelligence; Scanner; Field (mathematics); Cardiac imaging; Image segmentation; Data science; Machine learning; Medical physics; Data mining; Medicine; Radiology; Business","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01189963,0.002313073,0.002782593,0.002209149,0.001292848,0.00259083,0.002701397,0.00630351,0.00135775],"category_scores_gemma":[0.01739698,0.0009664914,0.002088277,0.002505319,0.001807618,0.001588879,0.003368895,0.002448096,0.001301119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001418846,"about_ca_system_score_gemma":0.00254132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006604106,"about_ca_topic_score_gemma":0.01062595,"domain_scores_codex":[0.9926348,0.00259288,0.0005131716,0.002341905,0.001345492,0.0005716697],"domain_scores_gemma":[0.9858094,0.00702713,0.0009899177,0.003060224,0.001614761,0.001498528],"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":[0.003743968,0.001092334,0.0426121,0.004223313,0.001967092,0.004853365,0.0018711,0.127025,0.03050783,0.006943156,0.2540901,0.5210707],"study_design_scores_gemma":[0.001049363,0.001453078,0.08810674,0.001108578,0.001314961,0.01819588,0.00360615,0.5797986,0.05844291,0.05856785,0.1876937,0.0006621434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5776192,0.03114733,0.304688,0.03082722,0.004696314,0.001124771,0.02970973,0.01055084,0.009636588],"genre_scores_gemma":[0.6928063,0.003955887,0.2391251,0.005111454,0.002516713,0.0005086229,0.04818746,0.002354595,0.005433865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01189963,"threshold_uncertainty_score":0.06293201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01784446899483396,"score_gpt":0.2571554625033866,"score_spread":0.2393109935085526,"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."}}