{"id":"W4311522567","doi":"10.3390/diagnostics12123031","title":"Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs","year":2022,"lang":"en","type":"article","venue":"Diagnostics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Optic disc; Optic cup (embryology); Convolutional neural network; Glaucoma; Fundus (uterus); Computer vision; Pattern recognition (psychology); Object detection; Fundus photography; Point (geometry); Cascade; Encoder; Retinal; Mathematics; Ophthalmology; Engineering","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":[0.004734122,0.00347587,0.001737307,0.003409624,0.000722151,0.001855446,0.001887214,0.002817461,0.001227962],"category_scores_gemma":[0.00647413,0.0005874929,0.001917741,0.00143548,0.0006285548,0.001486608,0.001266075,0.001239022,0.001030897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002519568,"about_ca_system_score_gemma":0.002168605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0406848,"about_ca_topic_score_gemma":0.03120081,"domain_scores_codex":[0.997717,0.000338048,0.000222537,0.0008554381,0.0005470148,0.0003200122],"domain_scores_gemma":[0.9975406,0.001067035,0.0002348988,0.0002584005,0.0006835938,0.0002154961],"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":[0.007870042,0.00145257,0.04853369,0.002309627,0.002871065,0.0008382994,0.00042263,0.2198924,0.0331283,0.001346341,0.03292953,0.6484055],"study_design_scores_gemma":[0.0001991158,0.001321444,0.01503595,0.0002192276,0.0005886135,0.0004922517,0.0002464424,0.9457483,0.0310178,0.000714676,0.004322675,0.0000935635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.891513,0.0287861,0.04332735,0.001638193,0.001478428,0.0005020088,0.005816494,0.0153115,0.01162693],"genre_scores_gemma":[0.9205252,0.003036899,0.05332723,0.0008038125,0.0002095256,0.0001531139,0.01623023,0.0004858298,0.005228214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0406848,"threshold_uncertainty_score":0.08089596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03688216591071442,"score_gpt":0.3173543325428143,"score_spread":0.2804721666320999,"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."}}