{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004393586,0.0001207095,0.0003956185,0.0002311022,0.00002440797,0.00001094965,0.00005565469,0.00005522354,0.0006245715],"category_scores_gemma":[0.0002534788,0.00009657897,0.00004135201,0.0003453804,0.0002828853,0.00009478704,0.00001827856,0.0001794362,0.00006989021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003882876,"about_ca_system_score_gemma":0.0000288502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009155074,"about_ca_topic_score_gemma":0.00005037903,"domain_scores_codex":[0.9985791,0.00005943857,0.0006247434,0.0002912627,0.0002948912,0.0001505083],"domain_scores_gemma":[0.9992511,0.000127157,0.0001787527,0.0002392593,0.0001275924,0.00007610075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001953778,0.0001348213,0.08919456,0.00006936271,0.00002729986,0.00001502801,0.001741551,0.00001432002,0.7938329,0.0008493754,0.0001236199,0.1138018],"study_design_scores_gemma":[0.0004660933,0.001714057,0.5027648,0.001617184,0.0003281099,0.00003462053,0.02833209,0.03401993,0.4174005,0.01272698,0.0002913521,0.0003042886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837819,0.0002883144,0.008296099,0.005420043,0.0001372879,0.000220089,0.000004318121,0.00002018249,0.001831778],"genre_scores_gemma":[0.9976435,0.0002188473,0.001526639,0.0001818699,0.0001409066,0.000007064238,0.00005583228,0.00001023517,0.0002151167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4135703,"threshold_uncertainty_score":0.683862,"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."}}