{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003199022,0.0003461655,0.0003042406,0.002211904,0.0001150406,0.0003625075,0.0001780391,0.0002799223,0.0004518502],"category_scores_gemma":[0.00106632,0.000138641,0.0003811907,0.0006139082,0.0001275504,0.0003891286,0.0001579514,0.0001712546,0.0002155968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430984,"about_ca_system_score_gemma":0.0001255282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001101959,"about_ca_topic_score_gemma":0.00141136,"domain_scores_codex":[0.9998347,0.00003612744,0.000009674231,0.00003199548,0.00007197027,0.0000155717],"domain_scores_gemma":[0.9997323,0.00009660772,0.00004546354,0.00002679644,0.00008245317,0.00001638209],"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":[0.0005418079,0.00009716934,0.01594315,0.000322608,0.0001260147,0.0004851689,0.0001824086,0.03257063,0.3644084,0.0009891207,0.0009522837,0.5833813],"study_design_scores_gemma":[0.0000212862,0.0003574164,0.0775568,0.00003972233,0.000158121,0.002647885,0.0002133085,0.7757286,0.1397949,0.001229136,0.002177967,0.00007485021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5904924,0.001411756,0.4046703,0.0001613704,0.00003144958,0.00005684918,0.0002270087,0.001179932,0.001768963],"genre_scores_gemma":[0.8308209,0.0005981758,0.1676926,0.00002876205,0.00002942223,0.00002092789,0.0002059918,0.00004611504,0.0005570764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002211904,"threshold_uncertainty_score":0.002191126,"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."}}