{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004227835,0.000974371,0.0004211084,0.0007904078,0.0002529019,0.0004054101,0.0009821932,0.0007399924,0.001641026],"category_scores_gemma":[0.0008183639,0.0003845592,0.0007659391,0.0004029025,0.0002651995,0.0006554883,0.0005727734,0.0008569101,0.0006589143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008878857,"about_ca_system_score_gemma":0.0006431046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009719205,"about_ca_topic_score_gemma":0.01452302,"domain_scores_codex":[0.9998329,0.00002999609,0.000005833905,0.00006040695,0.00003762852,0.00003318613],"domain_scores_gemma":[0.9998673,0.00003718585,0.00001843294,0.00002766498,0.00003746578,0.00001189028],"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":[0.0005598201,0.0002550401,0.004265893,0.0001804171,0.00038838,0.000400674,0.00009514095,0.301255,0.04155737,0.004855623,0.02248793,0.6236987],"study_design_scores_gemma":[0.00001676374,0.00006067978,0.0009622877,0.00001499331,0.00004273784,0.000117653,0.000009063081,0.9833105,0.01120246,0.001906371,0.002341631,0.00001478766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1756068,0.003235029,0.78994,0.001135802,0.000362283,0.0002738311,0.002192218,0.01718571,0.01006833],"genre_scores_gemma":[0.6986359,0.001321884,0.2757302,0.0008885601,0.0001306717,0.0002096986,0.005308733,0.000491762,0.01728257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009719205,"threshold_uncertainty_score":0.01932526,"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."}}