{"id":"W4252629173","doi":"10.32920/ryerson.14643936","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; Hough transform; Optic nerve; Image processing; Optic cup (embryology); Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033325,0.0006713655,0.000694552,0.001705184,0.0003742351,0.0009830858,0.000664719,0.0009101523,0.0008425949],"category_scores_gemma":[0.001759397,0.0002945592,0.0009612262,0.001162863,0.0002975797,0.0007255369,0.0005397435,0.0006998432,0.0004741961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004771973,"about_ca_system_score_gemma":0.0005487501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004608723,"about_ca_topic_score_gemma":0.003678504,"domain_scores_codex":[0.9994993,0.00008048238,0.0000246978,0.0001446584,0.0001825528,0.00006828718],"domain_scores_gemma":[0.9994844,0.0001044076,0.00009099696,0.00008399053,0.0002066343,0.00002947015],"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":[0.0002313131,0.0001688185,0.009153556,0.0001981557,0.0002163816,0.0002457265,0.0001186474,0.1002117,0.0371305,0.003974063,0.006374062,0.8419771],"study_design_scores_gemma":[0.000008805882,0.0001463359,0.01625895,0.00004212498,0.00009214692,0.000428399,0.00005159621,0.9485374,0.02667391,0.002938421,0.004781482,0.00004044337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1265088,0.00382975,0.8615346,0.0008445329,0.0002258826,0.0001259298,0.0004559199,0.00169086,0.004783829],"genre_scores_gemma":[0.6433854,0.003268478,0.3455417,0.0002525341,0.0002596257,0.0001171046,0.0008215762,0.0001070618,0.006246523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004608723,"threshold_uncertainty_score":0.009163797,"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."}}