{"id":"W3082904399","doi":"10.1117/1.jmi.7.4.044503","title":"Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Diabetic retinopathy; Medicine; Artificial intelligence; Deep learning; Fundus (uterus); Convolutional neural network; Retinal; End-to-end principle; Retinopathy; Pattern recognition (psychology); Computer science; Optometry; Machine learning; Ophthalmology; Diabetes mellitus","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001941345,0.0001682852,0.0005342799,0.0001376009,0.0002325831,0.00009691495,0.0002525224,0.00006482458,0.0001598],"category_scores_gemma":[0.01051237,0.0001355629,0.0002659769,0.0003649561,0.00009948867,0.0002301099,0.000105431,0.0008951951,0.00002083734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007827015,"about_ca_system_score_gemma":0.00009248815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002869386,"about_ca_topic_score_gemma":0.000003582513,"domain_scores_codex":[0.9973384,0.0001117287,0.0006736296,0.0002608555,0.001156381,0.0004590784],"domain_scores_gemma":[0.9978676,0.0003989906,0.0003307699,0.0001412251,0.0002654648,0.0009959679],"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":[0.0007728822,0.0001689911,0.04970847,0.0004250479,0.0002196896,0.001657149,0.002752101,0.0003705585,0.05268125,0.000002288214,0.004366955,0.8868746],"study_design_scores_gemma":[0.007130363,0.0007722249,0.005651948,0.00120287,0.0007794401,0.002003716,0.008023941,0.8134468,0.02900457,0.00004250905,0.1315193,0.0004224219],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5291383,0.001310556,0.4107636,0.05801205,0.0003996128,0.0002319356,0.00001456258,0.00006456681,0.00006490124],"genre_scores_gemma":[0.992646,0.0000955345,0.00258487,0.003420793,0.001142536,0.000006657647,0.00003063595,0.00003311803,0.00003985213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8864522,"threshold_uncertainty_score":0.9978225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755500321217589,"score_gpt":0.3195008864737511,"score_spread":0.3019458832615752,"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."}}