{"id":"W3212825636","doi":"","title":"Retinal Segmentation for Glaucoma Diagnosis Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Student Research Proceedings","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Glaucoma; Artificial intelligence; Thresholding; Deep learning; Computer science; Optic disc; Segmentation; Optic nerve; Computer vision; Pattern recognition (psychology); Optometry; Ophthalmology; Image (mathematics); Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004551553,0.0007592295,0.000535561,0.001737127,0.0004620595,0.0009692591,0.0008210352,0.0010185,0.002772664],"category_scores_gemma":[0.001192148,0.0004591986,0.001011539,0.0008048479,0.0003042867,0.0007280553,0.0006315356,0.00107092,0.001070142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419753,"about_ca_system_score_gemma":0.001045217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0130077,"about_ca_topic_score_gemma":0.02147134,"domain_scores_codex":[0.9997833,0.00002636544,0.00001570623,0.00006519638,0.00006483063,0.00004457563],"domain_scores_gemma":[0.9997066,0.00008593386,0.00004723206,0.00003741134,0.00009603984,0.0000268095],"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":[0.0004246826,0.0002748202,0.009324566,0.0001886932,0.0001967011,0.0002807713,0.00008713789,0.1588885,0.03106412,0.003427773,0.01145711,0.7843851],"study_design_scores_gemma":[0.00001199499,0.00005448184,0.001516018,0.00003710746,0.00002646036,0.00009714021,0.00001898029,0.985386,0.008751103,0.002497185,0.001592916,0.00001057329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1526737,0.005739906,0.8188131,0.002162698,0.0003719156,0.0002740911,0.001480636,0.009451649,0.009032151],"genre_scores_gemma":[0.6996437,0.0016789,0.288276,0.0006676106,0.000155895,0.0001139923,0.001854218,0.0002596805,0.007349873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0130077,"threshold_uncertainty_score":0.02586395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1359826484407935,"score_gpt":0.4858994374744733,"score_spread":0.3499167890336798,"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."}}