{"id":"W6945356789","doi":"10.24433/co.2116857.v1","title":"Code and data for Robust Segmentation of Optic Disc and Cup from Fundus Images Using Deep Neural Networks","year":2019,"lang":"en","type":"other","venue":"Code Ocean","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Optic disc; Convolutional neural network; Optic cup (embryology); Fundus (uterus); Segmentation; Residual","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":[0.0007097438,0.001429866,0.0004818121,0.001366128,0.0004054458,0.0008253602,0.002069063,0.001157817,0.05700984],"category_scores_gemma":[0.00518782,0.0005179338,0.0005774238,0.001114597,0.0004578138,0.0008422065,0.001825992,0.001228398,0.04984205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008024892,"about_ca_system_score_gemma":0.001612516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211901,"about_ca_topic_score_gemma":0.0227686,"domain_scores_codex":[0.9995152,0.00004318848,0.00004395864,0.0001241898,0.00022558,0.00004792653],"domain_scores_gemma":[0.9986389,0.0003943644,0.00009091105,0.0002934446,0.0004955404,0.0000867074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004322519,0.0001791607,0.001497334,0.0006723503,0.00005162234,0.0002143809,0.00006193529,0.01448598,0.003952283,0.003290928,0.887912,0.08724982],"study_design_scores_gemma":[0.00119515,0.0002810596,0.00675773,0.0004388505,0.00004850399,0.0007611304,0.00009218206,0.210845,0.03789797,0.02694051,0.7145466,0.0001953322],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.021043,0.0008143652,0.1087887,0.001346832,0.0009145517,0.001090734,0.6420892,0.1993467,0.02456592],"genre_scores_gemma":[0.03174761,0.000315805,0.1073166,0.0004928386,0.00007866872,0.00162181,0.8294254,0.01355295,0.01544823],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05700984,"threshold_uncertainty_score":0.1907169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07506801406436203,"score_gpt":0.3191470150309979,"score_spread":0.2440790009666359,"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."}}