{"id":"W4281562520","doi":"10.1049/ipr2.12537","title":"MCG&amp;BA‐Net: Retinal vessel segmentation using multiscale context gating and breakpoint attention","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Context (archaeology); Segmentation; Retinal; Breakpoint; Computer science; Image segmentation; Artificial intelligence; Chemistry; Biology; Biochemistry; Gene; Chromosomal translocation","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":[0.0004538786,0.0001572543,0.0002532488,0.0001409902,0.0006860818,0.0001580461,0.00005333843,0.00002592148,0.0002048624],"category_scores_gemma":[0.00007739162,0.0001581214,0.00007846347,0.0003172138,0.0001037428,0.0003174961,0.00008676357,0.000289878,0.000008891866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230976,"about_ca_system_score_gemma":0.00006034057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001656189,"about_ca_topic_score_gemma":0.000004046716,"domain_scores_codex":[0.9985933,0.00009760196,0.0003325039,0.0003601398,0.0003714524,0.000245009],"domain_scores_gemma":[0.9993563,0.00003088762,0.0002468082,0.0001269803,0.0001474588,0.00009157541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001119888,0.0001116595,0.0406074,0.0003613605,0.00004104905,0.00004595991,0.001636463,0.00003273291,0.8284232,8.764358e-7,0.0002552595,0.1283721],"study_design_scores_gemma":[0.01155899,0.0007331581,0.08981089,0.003771528,0.00298473,0.006637479,0.06946827,0.7451999,0.06353755,0.0005188593,0.003716048,0.002062564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515195,0.001024471,0.04574153,0.0009465649,0.0000504452,0.0001644153,0.00000906427,0.00007956957,0.0004644451],"genre_scores_gemma":[0.9533333,0.00001397656,0.04527966,0.0003326471,0.0001041905,0.00001934251,0.00008061037,0.00003277218,0.0008034763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7648857,"threshold_uncertainty_score":0.6448004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02551485247424242,"score_gpt":0.3222539083696157,"score_spread":0.2967390558953733,"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."}}