{"id":"W3082598879","doi":"10.1109/embc44109.2020.9175664","title":"Explainable Diabetic Retinopathy using EfficientNET","year":2020,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Diabetic retinopathy; Blindness; Convolutional neural network; Computer science; Retinopathy; Diabetes mellitus; Artificial intelligence; Deep learning; Medicine; Optometry","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":[0.00008396222,0.00007722207,0.0001890071,0.00004115485,0.00005044327,0.00002096139,0.00004370165,0.00002131914,0.0004142907],"category_scores_gemma":[0.0001216653,0.00005974802,0.00008084441,0.0003137003,0.00003133858,0.00002913259,0.00002719971,0.00008997278,0.0001361935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001780703,"about_ca_system_score_gemma":0.00002584014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000350218,"about_ca_topic_score_gemma":1.137137e-7,"domain_scores_codex":[0.9993192,0.00001932375,0.0001310035,0.0001885783,0.0001620748,0.0001798063],"domain_scores_gemma":[0.9996002,0.00001641534,0.00002716541,0.0001284066,0.00004980923,0.0001779472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003234014,0.0004857663,0.3798258,0.000818227,0.0002960109,0.001144597,0.002659881,0.002275312,0.5791261,0.0006398038,0.02301796,0.009387218],"study_design_scores_gemma":[0.002313482,0.0006701114,0.006310082,0.000234698,0.0009533087,0.0001363122,0.002148349,0.8546486,0.1089617,0.00005340982,0.02304629,0.0005236817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975021,0.0001491959,0.004952997,0.005544236,0.00002787435,0.00007457485,5.943142e-7,0.000107306,0.0141222],"genre_scores_gemma":[0.9917191,0.000004295391,0.003638882,0.002770602,0.0001147772,0.000001186093,0.000005017933,0.00001184559,0.001734351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8523733,"threshold_uncertainty_score":0.4536193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594965067966714,"score_gpt":0.2857072304884308,"score_spread":0.2397575798087637,"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."}}