{"id":"W2555769692","doi":"10.15353/vsnl.v1i1.63","title":"Superpixel-based Prostate Cancer Detection from Diffusion Magnetic Resonance Imaging","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Magnetic resonance imaging; Prostate cancer; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion MRI; Cancer; Cancer detection; Prostate; Computer science; Computation; Medicine; Artificial intelligence; Radiology; Internal medicine; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005301183,0.001120552,0.001037839,0.001995294,0.0003064376,0.0008480339,0.00128781,0.001067362,0.001842741],"category_scores_gemma":[0.001505451,0.0006611894,0.0009420315,0.001218744,0.0003773971,0.001015394,0.001067091,0.0008287015,0.0009267323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004450547,"about_ca_system_score_gemma":0.0005449189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451592,"about_ca_topic_score_gemma":0.005179221,"domain_scores_codex":[0.9994722,0.0000852866,0.00001887461,0.0001137892,0.0002618121,0.00004814178],"domain_scores_gemma":[0.9995484,0.0001758466,0.00006419134,0.00007417365,0.000106864,0.00003047281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003211781,0.0001148217,0.002117976,0.0005664537,0.000169956,0.0004221307,0.0001237874,0.07851309,0.1870349,0.004952552,0.005184353,0.7204788],"study_design_scores_gemma":[0.00002142307,0.00009730081,0.003000976,0.00003094796,0.0000784181,0.001059321,0.00002359254,0.9141867,0.07095008,0.005371651,0.005120005,0.00005945072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01383408,0.001712151,0.9817259,0.0001829882,0.0000497503,0.00007507301,0.0002073694,0.001266568,0.0009461092],"genre_scores_gemma":[0.17689,0.002199192,0.8167946,0.0003002649,0.0001602204,0.0001329601,0.0007395827,0.0003045504,0.002478542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002451592,"threshold_uncertainty_score":0.006164551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882292226003104,"score_gpt":0.3367763326941178,"score_spread":0.3079534104340868,"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."}}