{"id":"W4390229232","doi":"10.18280/ria.370626","title":"Image Transformers for Diabetic Retinopathy Detection from Fundus Datasets","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Diabetic retinopathy; Fundus camera; Fundus (uterus); Ophthalmology; Medicine; Transformer; Optometry; Computer science; Artificial intelligence; Diabetes mellitus; Ophthalmoscopy; Retinal; Engineering; Electrical engineering; Voltage; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003853524,0.0001554033,0.0002868177,0.0001901841,0.0001638392,0.00004789454,0.0001071616,0.0000733198,0.0002182649],"category_scores_gemma":[0.0002729942,0.0001455338,0.0002199028,0.0007199941,0.00009353508,0.000103879,0.00002014972,0.0001744087,0.001073484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004183558,"about_ca_system_score_gemma":0.0000244307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001122877,"about_ca_topic_score_gemma":0.00001401079,"domain_scores_codex":[0.9986344,0.00003223626,0.0003761674,0.0004285863,0.0001631423,0.0003654478],"domain_scores_gemma":[0.9991516,0.0002194809,0.00006986246,0.0003260399,0.00008723847,0.0001457627],"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":[0.0002741142,0.0001705884,0.001028219,0.0003249156,0.0001341454,0.00009147387,0.0009470304,0.000584119,0.6374857,0.00002356144,0.004700706,0.3542354],"study_design_scores_gemma":[0.0001343186,0.000217634,0.0003815201,0.0001661004,0.000257629,0.00001554747,0.00156608,0.520757,0.462499,0.000493952,0.01330994,0.0002013393],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6697542,0.0002738271,0.3233504,0.003382815,0.0004621364,0.0006920424,0.0004407333,0.0003756194,0.00126822],"genre_scores_gemma":[0.9957079,0.0001883904,0.001157676,0.0001438016,0.0001977937,0.00006987974,0.0008973968,0.0000332549,0.001603925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5201729,"threshold_uncertainty_score":0.9997043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04076199943553364,"score_gpt":0.3103736464002322,"score_spread":0.2696116469646985,"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."}}