{"id":"W2947820692","doi":"10.24908/iqurcp.13288","title":"Use of Convolutional Neural Network for Fully Automated Segmentation of Hard Exudates in Retinal Images","year":2019,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Segmentation; Diabetic retinopathy; Sørensen–Dice coefficient; Deep learning; Pattern recognition (psychology); Similarity (geometry); Process (computing); Blindness; Computer vision; Path (computing); Image segmentation; Medicine; Image (mathematics); Diabetes mellitus; Optometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007455191,0.0008860049,0.0004589339,0.001037215,0.0003580072,0.0008019117,0.0008534233,0.001010351,0.001068584],"category_scores_gemma":[0.001298184,0.0004077297,0.0006922656,0.0005567484,0.0002732901,0.0006127257,0.0005080809,0.000645509,0.0004470874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216324,"about_ca_system_score_gemma":0.001144833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02094241,"about_ca_topic_score_gemma":0.02060957,"domain_scores_codex":[0.9997072,0.00004646108,0.00002070206,0.00009419506,0.00007576427,0.0000556684],"domain_scores_gemma":[0.9996508,0.0001194977,0.00004193188,0.00004482454,0.0001172997,0.0000256255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008134405,0.0004661738,0.00728757,0.0002463672,0.0002607726,0.0005820486,0.0001698327,0.3958514,0.07291185,0.001613661,0.007808154,0.5119888],"study_design_scores_gemma":[0.00000850576,0.00006366253,0.001463358,0.00001508467,0.00001859105,0.00006587888,0.00001198119,0.9838541,0.01335685,0.000401594,0.0007284951,0.0000118263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4971645,0.002614933,0.4782273,0.0009158588,0.0003446815,0.0003420018,0.001827861,0.01112514,0.007437644],"genre_scores_gemma":[0.8120706,0.0005967449,0.1792743,0.000305794,0.00004107065,0.00009756264,0.002267555,0.0001703625,0.005176011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02094241,"threshold_uncertainty_score":0.04164106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1280928632963018,"score_gpt":0.3980077279243755,"score_spread":0.2699148646280737,"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."}}