{"id":"W3044753181","doi":"10.1002/path.5509","title":"Synthesis of diagnostic quality cancer pathology images by generative adversarial networks","year":2020,"lang":"en","type":"article","venue":"The Journal of Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; William Osler Health System; Canada's Michael Smith Genome Sciences Centre; Brampton Civic Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Adversarial system; Generative grammar; Pathology; Quality (philosophy); Cancer; Artificial intelligence; Computer science; Medicine; Internal medicine; Epistemology; Philosophy","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":[0.002049722,0.0008625087,0.000441331,0.000746216,0.0001498367,0.000698226,0.0006873593,0.0007653003,0.001511524],"category_scores_gemma":[0.006552786,0.0004487521,0.000745029,0.0003207095,0.0007389368,0.0005252115,0.001007889,0.001047997,0.0003363881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008235489,"about_ca_system_score_gemma":0.0004797701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001437252,"about_ca_topic_score_gemma":0.001254225,"domain_scores_codex":[0.9994618,0.0001897544,0.00002396785,0.0001269642,0.0001474932,0.00005005481],"domain_scores_gemma":[0.9972057,0.001849842,0.0003036658,0.000291836,0.0002745058,0.00007447813],"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":[0.0002322107,0.00005316665,0.00185517,0.00008488937,0.00005504457,0.0001486399,0.00007843289,0.9202229,0.01349365,0.002854056,0.001349048,0.05957282],"study_design_scores_gemma":[0.00001097849,0.00004592568,0.0004102437,0.00000948211,0.000009188499,0.00007219558,0.000008735628,0.9912223,0.005578585,0.002173103,0.0004505622,0.000008833679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09685212,0.0002714151,0.8989981,0.0004775865,0.0001084883,0.0001348875,0.0003132399,0.001137285,0.001706803],"genre_scores_gemma":[0.7978933,0.0002364251,0.1984047,0.0003386349,0.00005829455,0.000146295,0.0007943668,0.0001761551,0.001951832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002049722,"threshold_uncertainty_score":0.01084012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350190996187381,"score_gpt":0.2896147921778769,"score_spread":0.2661128822160032,"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."}}