{"id":"W2602001632","doi":"10.1007/s11548-017-1573-x","title":"Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Prostate cancer; Transfer of learning; Ultrasound; Cancer detection; Computer science; Cancer; Medicine; Radiology; Artificial intelligence; Internal medicine","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":[0.0006659254,0.0006447512,0.000460449,0.0006577242,0.0002110198,0.0003643786,0.0006749359,0.0007137974,0.001501696],"category_scores_gemma":[0.001999111,0.0001708026,0.0006500321,0.0005598203,0.0002085786,0.0006104346,0.0007941899,0.0007228336,0.0009288268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002241683,"about_ca_system_score_gemma":0.0005546065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003009878,"about_ca_topic_score_gemma":0.002612865,"domain_scores_codex":[0.9997911,0.00004656444,0.00001265769,0.00005171259,0.00004984699,0.00004820768],"domain_scores_gemma":[0.9993851,0.0002943163,0.00004269047,0.00006290515,0.0001794373,0.00003555402],"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":[0.0007820809,0.0005057451,0.006365375,0.0001367123,0.0001751143,0.0001588448,0.00006377898,0.07160224,0.0571847,0.0005601788,0.006498992,0.8559662],"study_design_scores_gemma":[0.00002618104,0.0002696693,0.00553123,0.00001495815,0.00009657613,0.0001434881,0.00004833711,0.9716471,0.01967711,0.001059349,0.001465199,0.00002073828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4343766,0.003173745,0.5525342,0.0007685525,0.0004176082,0.0001550525,0.0007543972,0.002963086,0.004856649],"genre_scores_gemma":[0.9400488,0.0007337367,0.0520466,0.0002455313,0.0001486752,0.00008261595,0.0008722838,0.000102875,0.005718858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003009878,"threshold_uncertainty_score":0.005984724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197309052454546,"score_gpt":0.3129923807567341,"score_spread":0.2910192902321887,"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."}}