{"id":"W4402904281","doi":"10.1109/jbhi.2024.3469630","title":"Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Info-communications Media Development Authority; National Research Foundation Singapore","keywords":"Diabetic retinopathy; Computer science; Adversarial system; Grading (engineering); Artificial intelligence; Retinopathy; Medicine; Diabetes mellitus; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148601,0.0007607525,0.0005289766,0.0005831465,0.0002831946,0.0005182442,0.0006363522,0.0008721756,0.0008053604],"category_scores_gemma":[0.004079083,0.0001776834,0.0005123114,0.000243828,0.0006326341,0.0007591307,0.0008931413,0.00126166,0.0002109544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008299015,"about_ca_system_score_gemma":0.0004070932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003832216,"about_ca_topic_score_gemma":0.002994171,"domain_scores_codex":[0.999211,0.0002274901,0.00004008396,0.0001836449,0.0002368191,0.0001008967],"domain_scores_gemma":[0.9987358,0.0005991354,0.0001818268,0.0002081564,0.0001867698,0.00008820993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001033965,0.0001930352,0.006451069,0.0001715311,0.0001683116,0.001036243,0.0001823739,0.6741812,0.02288196,0.007169103,0.008447505,0.2780838],"study_design_scores_gemma":[0.00001116509,0.00005648367,0.001081848,0.0000140748,0.00001893386,0.0001880282,0.00001244672,0.990502,0.00569177,0.001818167,0.0005949498,0.00001014888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4838519,0.002351782,0.497511,0.002214348,0.0004554773,0.0001751985,0.0005325323,0.002781847,0.0101259],"genre_scores_gemma":[0.9572388,0.000313257,0.0390536,0.0004650042,0.00007050767,0.00001956117,0.0002931855,0.00005602192,0.002490047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003832216,"threshold_uncertainty_score":0.007619858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04702073776878964,"score_gpt":0.3591808758883669,"score_spread":0.3121601381195773,"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."}}