{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001998046,0.0002785191,0.000367013,0.00007555346,0.0004832347,0.0001014541,0.0002782748,0.00008065868,0.0007188013],"category_scores_gemma":[0.00314814,0.0001961996,0.0001546152,0.0002593932,0.0002282733,0.0002461217,0.0005040832,0.0003674087,0.0003325939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008305198,"about_ca_system_score_gemma":0.000120629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006777023,"about_ca_topic_score_gemma":0.0006875278,"domain_scores_codex":[0.9973716,0.0003249511,0.0003806692,0.0007290117,0.0006910099,0.0005028221],"domain_scores_gemma":[0.9964425,0.001808557,0.00009492401,0.00121772,0.0002083787,0.0002279116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001481518,0.0005552598,0.008667246,0.00003473405,0.0001222713,0.0001386506,0.001149067,0.000008655786,0.778559,0.000002580298,0.1912289,0.0193855],"study_design_scores_gemma":[0.01143848,0.0003371364,0.5090483,0.0004754539,0.0002358886,0.00002476628,0.007527978,0.005941082,0.2198253,0.00002342894,0.2444639,0.0006582033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462501,0.005781266,0.000964987,0.03045125,0.001095451,0.00238368,0.01118648,0.0003396203,0.001547123],"genre_scores_gemma":[0.9804986,0.008036618,0.002524211,0.002718096,0.000474878,0.0002585903,0.004850925,0.00007102358,0.0005671283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5587336,"threshold_uncertainty_score":0.8000789,"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."}}