{"id":"W3215462356","doi":"10.18280/ts.380505","title":"Deep Att-ResGAN: A Retinal Vessel Segmentation Network for Color Fundus Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Guangdong Pharmaceutical University; National Natural Science Foundation of China","keywords":"Artificial intelligence; Segmentation; Computer science; Subnetwork; Preprocessor; Deep learning; Residual; Fundus (uterus); Generative adversarial network; Pattern recognition (psychology); Computer vision; Discriminator; Generalizability theory; Image segmentation; Mathematics; Algorithm; Ophthalmology; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003478198,0.0001721296,0.0003200172,0.00005759925,0.0001726155,0.00007276633,0.0000664948,0.0000492902,0.0009240774],"category_scores_gemma":[0.00005136249,0.0001573068,0.0002182817,0.0002801899,0.00005690517,0.00008401886,0.00002338749,0.000123445,0.00002917192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000706858,"about_ca_system_score_gemma":0.00009313315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001389227,"about_ca_topic_score_gemma":0.000005628125,"domain_scores_codex":[0.9985422,0.00007075829,0.0003515486,0.0003447242,0.0003312802,0.0003595057],"domain_scores_gemma":[0.9991964,0.0001271421,0.0001140207,0.0001580066,0.00025971,0.0001447222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003165493,0.001772271,0.0771274,0.001010393,0.002181017,0.0009875072,0.001436107,0.007056159,0.6130746,0.001074396,0.2024101,0.08870455],"study_design_scores_gemma":[0.0321318,0.005526023,0.1483789,0.001983789,0.01017245,0.001039527,0.008035173,0.2434257,0.4153715,0.001932996,0.1294903,0.002511813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6451648,0.002937598,0.3274402,0.01780271,0.0004247566,0.001380519,0.00006430237,0.0002626466,0.004522479],"genre_scores_gemma":[0.9655848,0.00007809539,0.02734915,0.00180384,0.001166825,0.0001359256,0.0004913306,0.00003535834,0.00335468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.32042,"threshold_uncertainty_score":0.9999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106448416780773,"score_gpt":0.2930215190736689,"score_spread":0.2719570349058612,"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."}}