{"id":"W3092679732","doi":"","title":"Robust classification of eye disease from fundus images using deep learning on multiple public datasets","year":2020,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fundus (uterus); Artificial intelligence; Computer science; Disease; Optometry; Deep learning; Ophthalmology; Pattern recognition (psychology); Medicine; Pathology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003787602,0.000176362,0.0003261366,0.0001855272,0.000294079,0.00005547005,0.0002952118,0.00005104675,0.0001039411],"category_scores_gemma":[0.006486079,0.000151966,0.00007380364,0.001223837,0.003074775,0.0003663978,0.0001421115,0.0003418132,0.00003428826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007893799,"about_ca_system_score_gemma":0.0003079439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001894161,"about_ca_topic_score_gemma":5.342997e-7,"domain_scores_codex":[0.9979399,0.0002369184,0.0003060122,0.0006958116,0.000494252,0.0003271027],"domain_scores_gemma":[0.9982691,0.0002697593,0.0002786876,0.0002460683,0.0002386796,0.0006976442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004772499,0.00009628507,0.47538,0.00001308209,0.00001898971,0.00009141262,0.0001804234,0.0003767238,0.5235642,0.00001217964,0.00001003825,0.0002089446],"study_design_scores_gemma":[0.0003612254,0.0004277416,0.7115737,0.00008875646,0.0001142651,0.00000890059,0.0005301856,0.2056163,0.08095468,0.0001411595,0.00002701273,0.000156154],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961003,0.000099334,0.0002126443,0.003118455,0.00004289206,0.0001350916,0.00006537612,0.00003783526,0.0001880755],"genre_scores_gemma":[0.9959102,0.000003366051,0.003364406,0.0003752316,0.0000817361,0.00000625221,0.0002257591,0.00001316209,0.00001991659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4426095,"threshold_uncertainty_score":0.9996383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.163892182337863,"score_gpt":0.3807594645937208,"score_spread":0.2168672822558578,"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."}}