{"id":"W2978639826","doi":"10.1109/jbhi.2019.2944643","title":"Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Histopathology; Prostate cancer; Grading (engineering); Artificial intelligence; Prostate; Computer science; Medicine; Radiology; Medical physics; Cancer; Pathology; Internal medicine","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":[0.0009528631,0.00007070593,0.0002577975,0.0001787078,0.00005497834,0.00001931793,0.0002393141,0.0000609214,0.000005575644],"category_scores_gemma":[0.00004721794,0.00005659332,0.00001899118,0.0001387363,0.0000961812,0.0006421262,0.00007262168,0.0001921548,6.63404e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009291753,"about_ca_system_score_gemma":0.0002126551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002375231,"about_ca_topic_score_gemma":0.00002042376,"domain_scores_codex":[0.9984464,0.00005461233,0.0009065613,0.00008480064,0.0003880853,0.0001195356],"domain_scores_gemma":[0.9979948,0.0001287956,0.001477854,0.0001591324,0.0001158541,0.000123512],"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":[0.00007048275,0.00002937117,0.007297749,0.000267016,0.000007272977,6.900393e-7,0.00275233,0.0005594819,0.0009410721,0.000006791198,0.0001403314,0.9879274],"study_design_scores_gemma":[0.002020565,0.001220736,0.03014468,0.0007241738,0.00001749638,0.0000373618,0.0004654395,0.9579898,0.004675317,0.0006939207,0.001922535,0.00008796738],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5144713,0.001488551,0.4827663,0.0006425263,0.0004894934,0.0001174177,0.00001519059,0.000005645883,0.0000035956],"genre_scores_gemma":[0.9066123,0.001872476,0.09127283,0.0001666926,0.0000575357,0.000001223563,0.00001038816,0.000003954136,0.000002564325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9878395,"threshold_uncertainty_score":0.2307809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124589333563531,"score_gpt":0.3210817892733692,"score_spread":0.2998358959377339,"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."}}