{"id":"W3198507998","doi":"10.32920/ryerson.14643936.v1","title":"Retinal Fundus image processing and ensemble learning: optic disc and optic cup detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Waterloo","keywords":"Optic disc; Adaptive histogram equalization; Fundus (uterus); Artificial intelligence; Computer science; Glaucoma; Optic disk; Thresholding; Segmentation; Retinal; Optic nerve; Image processing; Computer vision; Hough transform; Optic cup (embryology); Ophthalmology; Histogram equalization; Image (mathematics); Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003182231,0.0002804664,0.0005509112,0.0001766863,0.0001851598,0.0004647517,0.00004773041,0.000170996,0.00006734783],"category_scores_gemma":[0.0002929435,0.0002322795,0.0001149465,0.0001665609,0.0001483471,0.0001089653,0.0002868361,0.001051138,0.00000578361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005141823,"about_ca_system_score_gemma":0.0001084775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001859658,"about_ca_topic_score_gemma":0.000009781244,"domain_scores_codex":[0.9984195,0.00008334623,0.0002980334,0.0006852103,0.0002600774,0.0002538981],"domain_scores_gemma":[0.9991641,0.00004697787,0.0001594531,0.000236861,0.0002031791,0.0001894159],"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":[0.0006242989,0.0004958793,0.06585743,0.01674017,0.001194378,0.003244162,0.0049913,0.0003947172,0.5104805,0.00004471958,0.0002047785,0.3957277],"study_design_scores_gemma":[0.004468474,0.001472831,0.09935968,0.01355941,0.0112599,0.01248925,0.02477202,0.7636127,0.06392872,0.0006838248,0.001286743,0.003106399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973632,0.003034732,0.01555364,0.001050679,0.00005844755,0.0001610594,4.28913e-7,0.0001283326,0.006380621],"genre_scores_gemma":[0.9761984,0.0004514354,0.01478817,0.00004216358,0.0001610483,0.00001476438,0.0000343473,0.00003649649,0.008273197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.763218,"threshold_uncertainty_score":0.9472082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717511654486858,"score_gpt":0.2916609131241319,"score_spread":0.2744857965792633,"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."}}