{"id":"W2119496927","doi":"10.1109/tbme.2008.2009766","title":"Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Using SVM and RF Time Series","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of British Columbia","funders":"","keywords":"Cancer detection; Support vector machine; Prostate cancer; Ultrasound; Prostate; Radio frequency; Series (stratigraphy); Artificial intelligence; Cancer; Computer science; Medicine; Computer vision; Radiology; Internal medicine; Telecommunications; Biology","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":[0.00009726639,0.0001541862,0.0002491753,0.0004374769,0.00008253434,0.000007862123,0.00002927121,0.00008043337,0.0000336579],"category_scores_gemma":[0.00001239628,0.0001485482,0.00007821129,0.0005353685,0.0001910123,0.0001470455,4.937625e-7,0.0002837249,0.000001250395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005495266,"about_ca_system_score_gemma":0.00004062114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001653653,"about_ca_topic_score_gemma":0.000006795673,"domain_scores_codex":[0.9990675,0.0000111519,0.0002644745,0.000199934,0.0002108462,0.0002461051],"domain_scores_gemma":[0.9996578,0.00007537864,0.00003208466,0.00008354793,0.00002884373,0.0001223597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001047487,0.000118275,0.0005259968,0.0001332795,0.00006710416,0.00000998137,0.0006547191,0.004374685,0.9873914,1.411355e-7,0.000001271597,0.006618387],"study_design_scores_gemma":[0.002658474,0.0005546891,0.01362576,0.0009129206,0.0001999967,0.001897214,0.0001811285,0.01714914,0.9619878,0.000004258642,0.0004119135,0.000416708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8111899,0.0001983383,0.1880894,0.00006433803,0.0001980842,0.0001352967,0.00003103116,0.00008188184,0.00001167984],"genre_scores_gemma":[0.9960548,0.0004768014,0.003309632,0.0000115984,0.0000398715,0.00001801371,0.000002286188,0.00002646832,0.00006054725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1848648,"threshold_uncertainty_score":0.6057621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005903159123896471,"score_gpt":0.2186070927321336,"score_spread":0.2127039336082371,"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."}}