{"id":"W1493864766","doi":"10.1007/978-3-642-15711-0_6","title":"Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; NeuroRx Research (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conditional random field; Pattern recognition (psychology); Artificial intelligence; Markov random field; Segmentation; Linear discriminant analysis; Classifier (UML); Multiple sclerosis; Magnetic resonance imaging; Computer science; Medicine; Image segmentation; Radiology; Immunology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005058527,0.00009507889,0.000127734,0.0003792009,0.0002222826,0.00005989786,0.0002875336,0.00008311759,0.0000159079],"category_scores_gemma":[0.001394017,0.00009062177,0.00003792921,0.001392881,0.0003956107,0.0002868475,0.00006973494,0.0003862451,0.000003175172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005817365,"about_ca_system_score_gemma":0.00009809346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009540165,"about_ca_topic_score_gemma":0.001346767,"domain_scores_codex":[0.9987056,0.00007850464,0.0002697932,0.0004190306,0.0003044871,0.0002225592],"domain_scores_gemma":[0.998768,0.0007864151,0.0001103511,0.0002296785,0.00005577604,0.00004975185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001530897,0.00003559071,0.0008010071,0.000004483217,1.891856e-7,0.000001165565,0.0002149005,0.02063612,0.9264081,0.00003260183,1.452968e-7,0.05185043],"study_design_scores_gemma":[0.0003681946,0.00001818052,0.01408756,0.00002055549,6.495567e-7,0.00001482662,0.000001196849,0.3944206,0.5899036,0.001103586,0.00000209078,0.00005899047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4974195,0.000002132823,0.5019124,0.00007963836,0.0004787679,0.00008470061,0.000001082961,0.00001694744,0.000004839364],"genre_scores_gemma":[0.9891323,0.000001931162,0.01044979,0.0003248755,0.00007698642,0.000008108518,3.733954e-7,0.000005298682,3.455354e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4917127,"threshold_uncertainty_score":0.3695448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05526559040721297,"score_gpt":0.2715491983211502,"score_spread":0.2162836079139372,"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."}}