{"id":"W2107871626","doi":"10.1109/tmi.2009.2033909","title":"Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":516,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Hong Kong Government; Hong Kong Polytechnic University; National Eye Institute; Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Waikato Medical Research Foundation; Research to Prevent Blindness","keywords":"Computer science; Artificial intelligence; Fundus (uterus); Context (archaeology); Diabetic retinopathy; Data set; Test set; Software; Set (abstract data type); Computer vision; Task (project management); Medicine; Ophthalmology; Diabetes mellitus","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":[0.0002579782,0.0001932899,0.0004336509,0.0005958603,0.00006424224,0.00002514565,0.0001038682,0.00009421495,0.0002401369],"category_scores_gemma":[0.00009235063,0.000172463,0.0002436304,0.0007493182,0.0001721333,0.0001483604,0.000001015388,0.000675538,0.00002919844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553142,"about_ca_system_score_gemma":0.00007507411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005302,"about_ca_topic_score_gemma":0.00002267342,"domain_scores_codex":[0.9981696,0.00004446248,0.0005526445,0.0003226182,0.0006092435,0.0003014107],"domain_scores_gemma":[0.999191,0.0001135623,0.0001016334,0.0002537841,0.00009288147,0.000247123],"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":[0.0002022103,0.002507005,0.0007315404,0.0001360082,0.00006273788,0.0005034222,0.0003951513,0.0001185381,0.08657213,0.000001216832,0.0000303371,0.9087397],"study_design_scores_gemma":[0.01080509,0.002162067,0.02087737,0.004129255,0.001086168,0.002830302,0.003207195,0.6966403,0.256331,0.000420775,0.0004127197,0.001097758],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049274,0.000215685,0.08970091,0.003895439,0.0002084413,0.0002204505,0.000009598494,0.0001506409,0.0006713859],"genre_scores_gemma":[0.9987012,0.0001670668,0.0002914878,0.0005811506,0.00005103772,0.000009182267,0.000008867869,0.00001800036,0.0001719712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9076419,"threshold_uncertainty_score":0.7032838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105087549924263,"score_gpt":0.2763501090616983,"score_spread":0.265841354069272,"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."}}