{"id":"W95146287","doi":"10.1007/978-3-642-33418-4_47","title":"Hierarchical Conditional Random Fields for Detection of Gad-Enhancing Lesions in Multiple Sclerosis","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; NeuroRx Research (Canada); McGill University","funders":"","keywords":"Conditional random field; Voxel; Pattern recognition (psychology); Multiple sclerosis; Lesion; Artificial intelligence; Computer science; Probabilistic logic; Feature (linguistics); Medicine; 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":[0.001282799,0.0001081246,0.0001843762,0.0003911159,0.000125343,0.00006257004,0.0006679048,0.00008182546,0.000006060765],"category_scores_gemma":[0.0008671864,0.00009710433,0.00005267466,0.001025388,0.0002832857,0.000765614,0.0002250708,0.0002182263,0.000001545919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008732542,"about_ca_system_score_gemma":0.000101632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000421082,"about_ca_topic_score_gemma":0.00008812764,"domain_scores_codex":[0.9984198,0.00008696566,0.0003552665,0.0003436716,0.0004096875,0.0003845847],"domain_scores_gemma":[0.9978998,0.001519634,0.00009367982,0.0002786918,0.00009574267,0.0001124702],"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":[0.00002131785,0.0001477176,0.002215634,0.00002803804,0.000002271982,0.000001029185,0.001545352,0.00425844,0.2114621,0.0002519627,0.000006888781,0.7800593],"study_design_scores_gemma":[0.000664149,0.00007136,0.009491863,0.00006186577,0.000001023203,0.000004779333,9.125131e-7,0.3338866,0.6500598,0.005662051,0.000002943261,0.0000925899],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05377867,0.00004838572,0.9450524,0.0003025989,0.0004266735,0.0003259976,0.000002028034,0.00006054011,0.000002653818],"genre_scores_gemma":[0.5774403,0.000002755555,0.4221013,0.0003583993,0.0000606783,0.00003277168,0.000001171089,0.000002414278,1.668422e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7799667,"threshold_uncertainty_score":0.39598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03657310260315619,"score_gpt":0.291024444011149,"score_spread":0.2544513414079929,"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."}}