{"id":"W2995218128","doi":"10.1038/s41598-019-55972-4","title":"Prostate Cancer Detection using Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":205,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; University of Toronto; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"Government of Ontario; Ontario Institute for Cancer Research","keywords":"Convolutional neural network; Prostate cancer; Pipeline (software); Receiver operating characteristic; Object detection; Pattern recognition (psychology); Magnetic resonance imaging; Deep learning; CAD","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002904686,0.0001212998,0.000184422,0.00008720159,0.0001767404,0.00009167912,0.00002553329,0.00004880914,0.0003473939],"category_scores_gemma":[0.00001485294,0.00009851568,0.00009110603,0.0003043436,0.0001026629,0.0001416697,0.00003079603,0.000101818,0.00001507213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356516,"about_ca_system_score_gemma":0.0001672182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001517967,"about_ca_topic_score_gemma":0.00005803558,"domain_scores_codex":[0.9984902,0.00001649779,0.0002926987,0.0005558192,0.0003460042,0.0002988209],"domain_scores_gemma":[0.9990852,0.00001182325,0.0001862017,0.0003864476,0.000198922,0.0001314463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001061889,0.0001727029,0.9247257,0.00007483037,0.0001056083,0.0005457277,0.0002334234,0.02851548,0.02056032,0.00001278495,0.0007642967,0.02418297],"study_design_scores_gemma":[0.002233972,0.000417133,0.2410038,0.0003982159,0.0003914064,0.002319362,0.0001489153,0.6762811,0.04769014,0.001073871,0.02746669,0.0005753794],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858759,0.001940212,0.0002088034,0.0001427204,0.01081545,0.0007668561,0.000002531819,0.00005157518,0.0001959236],"genre_scores_gemma":[0.9979546,0.00003017567,0.0001071606,0.00009081397,0.0001466892,0.00007290271,0.00005183676,0.00001592702,0.001529881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6837218,"threshold_uncertainty_score":0.4017353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164072022919008,"score_gpt":0.275828026689475,"score_spread":0.2594208243975742,"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."}}