{"id":"W2082354808","doi":"10.1109/tmi.2007.911547","title":"Prostate Cancer Spectral Multifeature Analysis Using TRUS Images","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agfa-Gevaert (Canada); University of Waterloo","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Feature extraction; Support vector machine; Gabor filter; Particle swarm optimization; Feature (linguistics); Classifier (UML); Computer vision; Feature vector; Algorithm","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":[0.0004467555,0.0002288548,0.0003369428,0.0004795701,0.0005308474,0.0001251831,0.000577367,0.00008036038,0.0002332827],"category_scores_gemma":[0.00002596633,0.0001999408,0.0003015201,0.001614284,0.0002155743,0.000623971,0.000005136179,0.0006707556,0.00001735731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001273567,"about_ca_system_score_gemma":0.0002405418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004708323,"about_ca_topic_score_gemma":0.00002485049,"domain_scores_codex":[0.9975762,0.0002219388,0.0003117018,0.0005470672,0.0008515466,0.0004915746],"domain_scores_gemma":[0.9988964,0.0001719402,0.00007335968,0.000440156,0.0001061903,0.0003120025],"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":[0.0001250021,0.000633003,0.001745205,0.00005118351,0.001023019,0.003669258,0.006576983,0.07488985,0.04075546,0.00005074287,0.001034563,0.8694457],"study_design_scores_gemma":[0.001130574,0.00002346902,0.001394809,0.0000738227,0.0003030133,0.0003695892,0.00004693247,0.9117773,0.08406869,0.0001181359,0.0002711403,0.0004225153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02433711,0.0003652329,0.9723086,0.00165644,0.0008132727,0.000108899,0.000009948712,0.000230143,0.0001703701],"genre_scores_gemma":[0.8726339,0.0002396108,0.1249975,0.001291184,0.0001388599,0.00001675167,0.000001080915,0.00002111755,0.0006600004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8690232,"threshold_uncertainty_score":0.815335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084669043114202,"score_gpt":0.3142411803718369,"score_spread":0.2933944899406949,"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."}}