{"id":"W2166636660","doi":"10.1109/icpr.2006.318","title":"Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information","year":2006,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Posterior probability; Voxel; Bayesian probability; Entropy (arrow of time); Principle of maximum entropy; Multivariate statistics; Spatial analysis; Probabilistic logic; Mathematics; Machine learning; Statistics","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":[0.00007331317,0.0000805143,0.00005016004,0.00004767529,0.00008112299,0.00003259961,0.00005387569,0.0001128441,0.00001840061],"category_scores_gemma":[0.000006028805,0.00007258061,0.00002501114,0.00004631076,0.00003478661,0.00001377382,0.00002577804,0.00003934428,0.000007554951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001397195,"about_ca_system_score_gemma":0.00003788984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002809819,"about_ca_topic_score_gemma":0.0001080263,"domain_scores_codex":[0.9994227,0.00002083421,0.0001807166,0.0001627946,0.0001161359,0.00009679825],"domain_scores_gemma":[0.9996776,0.000001969852,0.00005754557,0.0001516208,0.00007145933,0.000039818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001778262,0.00006253602,0.001931676,0.00002542225,0.000009374123,1.725859e-7,0.00004557696,0.008067047,0.8247799,0.004843627,0.01933594,0.1407209],"study_design_scores_gemma":[0.0008691219,0.0001030248,0.01538074,0.00001639838,0.00001103195,0.00001249425,0.0003546991,0.8313577,0.06209272,0.001008784,0.08849796,0.0002953496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069483,0.00009176529,0.8879185,0.0005184027,0.00005230401,0.00008905818,0.000001835498,0.00001736171,0.004362461],"genre_scores_gemma":[0.9979765,0.00006998546,0.0008190135,0.0002488146,0.0001600954,0.00001763475,0.0003998726,0.000006015412,0.0003020304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8910282,"threshold_uncertainty_score":0.2959751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01841240055342877,"score_gpt":0.2455650570422901,"score_spread":0.2271526564888613,"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."}}