{"id":"W4223572678","doi":"10.1038/s41598-022-09675-y","title":"State-of-the-art retinal vessel segmentation with minimalistic models","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"DiagnoCure (Canada); École de Technologie Supérieure","funders":"Marie Curie","keywords":"Computer science; Segmentation; Benchmark (surveying); Artificial intelligence; Context (archaeology); Convolutional neural network; Task (project management); Pattern recognition (psychology); Domain adaptation; Deep learning; Domain (mathematical analysis); Machine learning","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":[0.001688238,0.002101015,0.001668314,0.001222994,0.0006222839,0.002186792,0.003095088,0.002168876,0.003466303],"category_scores_gemma":[0.003834166,0.001055971,0.002229639,0.0009466923,0.0007096159,0.00239391,0.001503925,0.002614875,0.002703246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001611552,"about_ca_system_score_gemma":0.00205822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0152461,"about_ca_topic_score_gemma":0.0225573,"domain_scores_codex":[0.9991344,0.0001840855,0.0000559792,0.0003450545,0.000192826,0.00008765932],"domain_scores_gemma":[0.9988887,0.0004925078,0.00008912302,0.0002979458,0.0001612949,0.00007042671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006967537,0.0002779795,0.002563576,0.0009486018,0.0007338861,0.000197656,0.0001467001,0.5554402,0.0160333,0.008582515,0.01843621,0.3959426],"study_design_scores_gemma":[0.00001512074,0.00007416176,0.0005077412,0.00005344535,0.00004917338,0.00008756268,0.00001600163,0.9854736,0.00449934,0.006100782,0.003101198,0.0000219149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09019682,0.01340628,0.861051,0.001533928,0.0003122467,0.0002343856,0.002653522,0.01981417,0.01079758],"genre_scores_gemma":[0.4308713,0.005521567,0.5342773,0.001309204,0.0002712643,0.0002982786,0.01350681,0.002345643,0.01159858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0152461,"threshold_uncertainty_score":0.03031468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01578420207779882,"score_gpt":0.2614645849652535,"score_spread":0.2456803828874547,"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."}}