{"id":"W2915212708","doi":"10.1016/j.artmed.2019.02.004","title":"Joint segmentation and classification of retinal arteries/veins from fundus images","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":105,"is_retracted":false,"has_abstract":false,"ca_institutions":"St Mary's Hospital Centre; Polytechnique Montréal","funders":"","keywords":"Fundus (uterus); Segmentation; Joint (building); Diabetic retinopathy; Retinal; Image segmentation; Track (disk drive)","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":[0.000678742,0.0005900926,0.0008328616,0.003379091,0.0003690136,0.001716861,0.0004549335,0.000959569,0.001214086],"category_scores_gemma":[0.001172952,0.0003296781,0.0008325732,0.001443011,0.0003050486,0.0004894478,0.000468279,0.000448949,0.0009141802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003863224,"about_ca_system_score_gemma":0.0009001447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006408185,"about_ca_topic_score_gemma":0.008945303,"domain_scores_codex":[0.999645,0.00005395364,0.00002487273,0.00008408915,0.0001008866,0.00009107887],"domain_scores_gemma":[0.9996146,0.00006661548,0.00005478088,0.00007046614,0.0001461317,0.00004735617],"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":[0.001029856,0.000242618,0.02307971,0.0002310014,0.0002494402,0.0004678271,0.000205579,0.0144406,0.2536225,0.001627709,0.004928692,0.6998744],"study_design_scores_gemma":[0.00005515046,0.0003393803,0.08386928,0.00007812638,0.0004440053,0.001638743,0.00031447,0.7082393,0.1944587,0.003688125,0.006812141,0.00006265442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5406666,0.002847715,0.4473159,0.0004315884,0.0001586227,0.0002034848,0.001293216,0.003209609,0.00387313],"genre_scores_gemma":[0.8065243,0.001079529,0.1848291,0.00007299567,0.00009739389,0.0000523027,0.001564871,0.0002114695,0.005568186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006408185,"threshold_uncertainty_score":0.01274174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07126866799630192,"score_gpt":0.3514150413634922,"score_spread":0.2801463733671902,"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."}}