{"id":"W2575552683","doi":"10.1016/j.neuroimage.2016.12.064","title":"Longitudinal multiple sclerosis lesion segmentation: Resource and challenge","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":356,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Institute of Neurological Disorders and Stroke; Medical Research Council; National Institutes of Health; Multiple Sclerosis Society; University College London; Brain Research Trust; University College London Hospitals NHS Foundation Trust; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research","keywords":"Segmentation; Computer science; Consistency (knowledge bases); Data set; Artificial intelligence; Resource (disambiguation); Test (biology); Set (abstract data type); Machine learning; Medical physics; Data mining; Medicine","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":[0.0002124898,0.000161329,0.0002504407,0.00007483317,0.0008378457,0.0001489311,0.0001618622,0.00004907457,0.00005621251],"category_scores_gemma":[0.0009996811,0.000138673,0.00006273109,0.0000375305,0.0003358528,0.0002211688,0.0003911094,0.0002357017,0.00005060828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003146698,"about_ca_system_score_gemma":0.00001762347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007270488,"about_ca_topic_score_gemma":0.00004349302,"domain_scores_codex":[0.9986132,0.00005354157,0.0001712595,0.0004399515,0.0004181954,0.0003038342],"domain_scores_gemma":[0.9987767,0.0001418678,0.00009862517,0.0007042042,0.00008585513,0.0001927834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005886309,0.0003779886,0.4385131,0.0003556792,0.00009085277,0.0002479134,0.0008669164,0.000001804453,0.3441391,0.0000318286,0.0110087,0.2037774],"study_design_scores_gemma":[0.002478187,0.0002764362,0.9854879,0.0001246409,0.00002846744,0.00001658119,0.000137016,0.0003913286,0.00519474,0.000004873187,0.00574738,0.0001124066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793535,0.0003904197,0.00006290158,0.006708755,0.0001069232,0.0005485682,0.00001745949,0.00008560366,0.01272585],"genre_scores_gemma":[0.9957612,0.002258532,0.0008607148,0.0001679757,0.0001723349,0.00003563051,0.000007971391,0.00002778088,0.0007078375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5469748,"threshold_uncertainty_score":0.6444116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2285605769051656,"score_gpt":0.3632779226111466,"score_spread":0.134717345705981,"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."}}