{"id":"W3204149255","doi":"10.1016/j.neuroimage.2021.118589","title":"Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset","year":2021,"lang":"en","type":"article","venue":"NeuroImage","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Agence Nationale de la Recherche; Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques","keywords":"Computer science; Segmentation; Artificial intelligence; Fluid-attenuated inversion recovery; Task (project management); Protocol (science); Pattern recognition (psychology); Scanner; Machine learning; Magnetic resonance imaging; Medicine; Radiology; Pathology","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":[0.00390476,0.005258744,0.003130275,0.004809161,0.001633885,0.002526114,0.004428283,0.00556984,0.006201092],"category_scores_gemma":[0.007487208,0.0009143797,0.003230134,0.003175065,0.0009807943,0.001130421,0.003031037,0.002287913,0.008777486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002091572,"about_ca_system_score_gemma":0.002460869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01506956,"about_ca_topic_score_gemma":0.02832174,"domain_scores_codex":[0.9967128,0.0005914494,0.0004589738,0.0009828217,0.0008975873,0.0003564324],"domain_scores_gemma":[0.9971438,0.0005891687,0.000241074,0.0008631703,0.000799223,0.0003634611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002888152,0.001531026,0.01041568,0.004083564,0.001788502,0.002271878,0.0003521541,0.01233028,0.01246013,0.0008635264,0.8207331,0.130282],"study_design_scores_gemma":[0.003988523,0.002365578,0.1089128,0.00230648,0.001977205,0.02292616,0.001654245,0.1463689,0.05215074,0.009325461,0.6472027,0.0008212884],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2022669,0.0161059,0.02235622,0.00411228,0.002762684,0.003550518,0.7102374,0.02546639,0.0131416],"genre_scores_gemma":[0.06209115,0.001193888,0.02567453,0.0005690305,0.0003699956,0.0009065676,0.9045353,0.000902079,0.003757518],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01506956,"threshold_uncertainty_score":0.02996373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1569568070409589,"score_gpt":0.3383751406200662,"score_spread":0.1814183335791074,"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."}}