{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001494527,0.0001230068,0.0002551891,0.0002535944,0.0004863452,0.0002160377,0.0001150726,0.00005281557,0.00008678471],"category_scores_gemma":[0.00120827,0.0001132435,0.000150678,0.000820033,0.00009578029,0.0001494786,0.0001495594,0.0004774591,0.00001717473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002322095,"about_ca_system_score_gemma":0.00009781709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004798705,"about_ca_topic_score_gemma":0.000003693873,"domain_scores_codex":[0.9976485,0.00004860726,0.0002528297,0.0004349634,0.001092803,0.000522254],"domain_scores_gemma":[0.9975802,0.0001628835,0.00006657073,0.00008689926,0.001926323,0.0001770892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009277865,0.0002161401,0.841633,0.0003363541,0.0001428077,0.0000503507,0.00173672,0.000001430899,0.1484415,0.0001925889,0.000846452,0.006309906],"study_design_scores_gemma":[0.007430263,0.002823203,0.5065147,0.002508563,0.001278929,0.0005292347,0.1204222,0.022269,0.2983991,0.001272842,0.03561784,0.0009341105],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940918,0.0004524988,0.0004633929,0.001890236,0.00003249561,0.0004460383,8.822681e-7,0.0000551924,0.002567433],"genre_scores_gemma":[0.9876221,0.0003721839,0.007686478,0.00009595844,0.0003946404,0.0002471631,0.0000278782,0.00003281671,0.003520717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3351183,"threshold_uncertainty_score":0.4617935,"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."}}