{"id":"W2771573249","doi":"10.15353/vsnl.v3i1.182","title":"Automated Screening for Diabetic Retinopathy Using Compact Deep Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Nvidia","keywords":"Diabetic retinopathy; Blindness; Retinopathy; Medicine; Fundus (uterus); Diabetes mellitus; Optometry; Retinal; Retina; Ophthalmology; Intensive care medicine; Computer science; Neuroscience; Psychology; Endocrinology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005363117,0.0006079942,0.0003737306,0.0006228425,0.0002416218,0.0005130107,0.0006998365,0.0005671356,0.001403918],"category_scores_gemma":[0.001797181,0.0002855653,0.0004238367,0.0003567371,0.0002055473,0.0007446951,0.0005843878,0.0006418738,0.0002834004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007496615,"about_ca_system_score_gemma":0.000468818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01157419,"about_ca_topic_score_gemma":0.01420476,"domain_scores_codex":[0.9997988,0.00004071669,0.00001044061,0.00005947028,0.00004800724,0.0000425287],"domain_scores_gemma":[0.9995123,0.0002399809,0.00005746731,0.00005218848,0.0001101382,0.00002798095],"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":[0.001108733,0.0007100118,0.01674544,0.000171678,0.0003021048,0.0004228432,0.000109192,0.382861,0.02947125,0.0017723,0.00845158,0.557874],"study_design_scores_gemma":[0.00001025114,0.00005371754,0.001622998,0.000008670986,0.00001785754,0.00004174492,0.0000114015,0.9942243,0.003092769,0.0006360215,0.0002740262,0.000006278914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6551164,0.002482909,0.3291329,0.001472445,0.0002292163,0.0001091419,0.001141843,0.004954887,0.005360204],"genre_scores_gemma":[0.9562965,0.0003043201,0.04035408,0.0002642757,0.00004219761,0.00002819467,0.0006834221,0.00003597415,0.001990987],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01157419,"threshold_uncertainty_score":0.02301359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637068858273546,"score_gpt":0.3566245915677359,"score_spread":0.3302539029850005,"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."}}