{"id":"W2208471067","doi":"10.3389/fnins.2015.00456","title":"Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Agence Nationale de la Recherche; Canadian Institutes of Health Research; Multiple Sclerosis Society; Multiple Sclerosis Society of Canada","keywords":"Inpainting; Lesion; Magnetic resonance imaging; Hyperintensity; Artificial intelligence; Segmentation; White matter; Medicine; Computer science; Pattern recognition (psychology); Computer vision; Nuclear medicine; Radiology; Pathology; Image (mathematics)","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":[0.001126927,0.0001127487,0.0001991421,0.0004112205,0.00005075554,0.00009067589,0.00117557,0.00004237445,0.000001083857],"category_scores_gemma":[0.0006058271,0.0001089576,0.00002592815,0.001671351,0.0005215795,0.001257671,0.000337457,0.0002333899,0.000001185044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022049,"about_ca_system_score_gemma":0.000245696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007138048,"about_ca_topic_score_gemma":0.00001017651,"domain_scores_codex":[0.9981006,0.0000869143,0.0003939263,0.0004882522,0.0005867722,0.0003435725],"domain_scores_gemma":[0.9992895,0.00002731458,0.0001411747,0.0003062375,0.00008499393,0.0001508111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002731437,0.0006601643,0.2147925,0.0001681299,0.000001474384,0.0004605292,0.008412634,0.001495726,0.06829541,0.0005842851,0.01275807,0.6923437],"study_design_scores_gemma":[0.0005912864,0.000151515,0.04760772,0.0002096197,0.000001869993,0.00001921117,0.0005758224,0.8869849,0.06162924,0.001881348,0.00009839109,0.0002490077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01481677,0.00003993214,0.9834897,0.000245777,0.0005192388,0.0001662736,5.595815e-7,0.00007146702,0.0006502974],"genre_scores_gemma":[0.5785082,0.000006990567,0.4212308,0.0002016734,0.000009054241,0.0000118563,2.390501e-7,0.000004809515,0.00002633148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8854892,"threshold_uncertainty_score":0.4443161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03444150010617871,"score_gpt":0.303667359261153,"score_spread":0.2692258591549743,"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."}}