{"id":"W3087137785","doi":"10.1200/cci.20.00035","title":"Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor–Educated Neural Network","year":2020,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Brain Tumour Charity","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Cluster analysis; Feature (linguistics); Context (archaeology); Pattern recognition (psychology); Deep learning; Machine learning; Biology","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":[0.0009396786,0.0002035608,0.0004883381,0.00007919359,0.00008252494,0.00004736456,0.0007982889,0.0001641362,0.00002008148],"category_scores_gemma":[0.0002138234,0.0001994287,0.0001676722,0.001074816,0.000150395,0.0005616595,0.0003675029,0.000560364,0.000008810137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003370839,"about_ca_system_score_gemma":0.0004731761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003339193,"about_ca_topic_score_gemma":0.0001104431,"domain_scores_codex":[0.9969038,0.0002718477,0.001730097,0.0002871643,0.0003768522,0.0004302743],"domain_scores_gemma":[0.9981034,0.0003022434,0.0007656714,0.0004767113,0.0001456003,0.0002064045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007876141,0.0003413506,0.2585227,0.0008023877,0.0000429192,0.00005554868,0.006736157,0.6925271,0.00180217,0.000263317,0.01434178,0.02377691],"study_design_scores_gemma":[0.001145222,0.0004813969,0.02698278,0.00004323535,0.00001602779,0.000009772798,0.0000604435,0.9683917,0.001309443,0.00008277347,0.001244453,0.0002327748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9488071,0.0003182095,0.04828427,0.001003013,0.0009692824,0.0003224438,0.00001157239,0.00008936933,0.0001948045],"genre_scores_gemma":[0.9696891,0.00002558075,0.02440495,0.00553656,0.0003046722,0.00001803305,0.000004901988,0.00001225022,0.000004002608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2758645,"threshold_uncertainty_score":0.8132465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325694908421961,"score_gpt":0.3537271518511458,"score_spread":0.2211576610089497,"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."}}