{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121162,0.000665064,0.0005167589,0.002297986,0.0002426741,0.0009704511,0.0006316003,0.000528213,0.004004861],"category_scores_gemma":[0.00549309,0.0002324596,0.0006117221,0.001514975,0.0002291551,0.001095622,0.0007138113,0.0005858889,0.001256566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566579,"about_ca_system_score_gemma":0.000571909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003111862,"about_ca_topic_score_gemma":0.002713513,"domain_scores_codex":[0.9996234,0.0000735948,0.00004191178,0.00009373645,0.0001198214,0.00004743274],"domain_scores_gemma":[0.9988266,0.0004792403,0.0000867328,0.0002852284,0.0002676268,0.00005464918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002743492,0.0003215958,0.007153363,0.0004254603,0.0001859142,0.0004142131,0.0001116967,0.03142464,0.05131748,0.005618117,0.01377177,0.8865123],"study_design_scores_gemma":[0.0001805246,0.0004103968,0.009049406,0.00006114956,0.0001342646,0.0007847997,0.0001479769,0.8894665,0.08076732,0.01210454,0.006858059,0.00003501738],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2595851,0.001819348,0.6960089,0.0004600536,0.000145816,0.0004184636,0.007570199,0.03073437,0.003257837],"genre_scores_gemma":[0.787127,0.0008136556,0.1998979,0.00008606637,0.00004194456,0.0001314128,0.00993315,0.000468268,0.001500562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004004861,"threshold_uncertainty_score":0.01339763,"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."}}