{"id":"W2021727929","doi":"10.1109/tbme.2015.2404300","title":"Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Vancouver General Hospital; London Health Sciences Centre; Kingston General Hospital; Robarts Clinical Trials; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Pattern recognition (psychology); Artificial intelligence; Feature selection; Computer science; Independent component analysis; Wavelet; Feature vector; Joint (building); Fractal dimension; Principal component analysis; Feature extraction; Ultrasound; Radio frequency; Fractal; Mathematics; Medicine; Engineering; Telecommunications; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009583589,0.0006600589,0.0004770533,0.001298803,0.0001552382,0.0005709939,0.0003181921,0.0004501589,0.0007536052],"category_scores_gemma":[0.002352049,0.0001459257,0.0004679699,0.00072308,0.0003906824,0.000485426,0.0003788854,0.0005521575,0.0002720905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001845754,"about_ca_system_score_gemma":0.0003641807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006560502,"about_ca_topic_score_gemma":0.0009556951,"domain_scores_codex":[0.9996132,0.00009889533,0.00002226171,0.00007170498,0.0001650616,0.00002887269],"domain_scores_gemma":[0.9993396,0.0002926647,0.00009126378,0.00008007855,0.0001659311,0.00003036087],"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":[0.0005888788,0.0002547008,0.02199549,0.0004203268,0.0002362659,0.000216102,0.0002106877,0.04382585,0.5448507,0.001321949,0.001215276,0.3848637],"study_design_scores_gemma":[0.00004042643,0.0006824929,0.09172372,0.0000515111,0.0003056576,0.001472571,0.0001179099,0.6475949,0.2508025,0.002713228,0.004362965,0.0001321806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2148822,0.00128128,0.7814345,0.0002048887,0.00005232951,0.00009161702,0.0002913444,0.000764691,0.0009971445],"genre_scores_gemma":[0.7128054,0.0008182448,0.2846874,0.00007746022,0.0001002561,0.0001180936,0.0004713852,0.00009945027,0.0008223432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001298803,"threshold_uncertainty_score":0.005068302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263006314913907,"score_gpt":0.2592885008049446,"score_spread":0.2329878693135539,"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."}}