{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005519597,0.0007976815,0.0005160226,0.0008532387,0.0002092551,0.0005041612,0.0006895025,0.0005541563,0.001105023],"category_scores_gemma":[0.001195798,0.0003826463,0.0005835568,0.0005473684,0.0001658258,0.000427985,0.0005476719,0.000614315,0.0005059473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008962363,"about_ca_system_score_gemma":0.0009208066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0176843,"about_ca_topic_score_gemma":0.02583809,"domain_scores_codex":[0.9997161,0.00004188022,0.00001443596,0.00008841708,0.00008097375,0.00005821568],"domain_scores_gemma":[0.9996761,0.0001193907,0.00005055088,0.0000293199,0.0001032757,0.0000213883],"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":[0.0004694561,0.0002933632,0.01343231,0.0002125014,0.0003157125,0.0002723394,0.00005681798,0.2382484,0.02445263,0.001509946,0.008329916,0.7124066],"study_design_scores_gemma":[0.00001257558,0.00006555561,0.002865257,0.00001714909,0.0000392286,0.0001007699,0.00000774351,0.9879788,0.006447667,0.001031239,0.001417447,0.00001649874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2153466,0.007186003,0.7595534,0.0009649883,0.0002468105,0.0002287867,0.00189382,0.008222792,0.006356753],"genre_scores_gemma":[0.8363968,0.001805198,0.152938,0.0004591074,0.0001095747,0.0001313151,0.002763343,0.0001091983,0.005287493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0176843,"threshold_uncertainty_score":0.03516269,"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."}}