{"id":"W2940432231","doi":"10.1007/s11548-019-01967-5","title":"Prostate cancer detection using residual networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"EVRAZ (Canada)","funders":"National Cancer Institute; Memorial Sloan-Kettering Cancer Center","keywords":"Jaccard index; Residual; Prostate cancer; Segmentation; Computer science; Diffusion MRI; Artificial intelligence; Effective diffusion coefficient; Prostate; Perineural invasion; Magnetic resonance imaging; Radiology; Medicine; Lesion; Pattern recognition (psychology); Cancer; Pathology; Algorithm; Internal medicine","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":[0.0002440333,0.00009156981,0.0003223026,0.0001961641,0.00002690323,0.00002213398,0.00004556324,0.00007309502,0.00003625184],"category_scores_gemma":[0.00001129502,0.00006928229,0.000114465,0.00005424668,0.0000437027,0.0001050667,0.00002132538,0.0001705073,0.000001078375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367065,"about_ca_system_score_gemma":0.0001302743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001676782,"about_ca_topic_score_gemma":0.000002601128,"domain_scores_codex":[0.999193,0.00006685855,0.00035213,0.0001178275,0.000151409,0.0001188281],"domain_scores_gemma":[0.9990661,0.0002387304,0.0002740717,0.00005593673,0.0002929828,0.00007222276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001012568,0.00009981879,0.8544327,0.00001652369,0.001094096,0.0004658867,0.00007974784,0.003033165,0.0006927532,0.00001396111,0.0008680453,0.1381907],"study_design_scores_gemma":[0.001747663,0.0003401074,0.9725167,0.0004494778,0.0001322348,0.007176856,0.00001605835,0.01375451,0.0006997627,0.00004369764,0.003010997,0.0001119872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894363,0.003000633,0.002751433,0.001245532,0.003449561,0.00008625349,0.000003512421,0.000007364434,0.00001946724],"genre_scores_gemma":[0.995582,0.002070933,0.0005631145,0.000717244,0.001021374,0.000002905923,0.000006896463,0.000008321862,0.00002717995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1380787,"threshold_uncertainty_score":0.282525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02015408334373452,"score_gpt":0.288080349281952,"score_spread":0.2679262659382175,"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."}}