{"id":"W3206468898","doi":"10.1016/j.xops.2021.100069","title":"Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data","year":2021,"lang":"en","type":"article","venue":"Ophthalmology Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC; Compute Canada","keywords":"Artificial intelligence; Segmentation; Diabetic retinopathy; Receiver operating characteristic; F1 score; Computer science; Medicine; Deep learning; Retinal; Pattern recognition (psychology); Machine learning; Ophthalmology; Diabetes mellitus","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004446114,0.0009070794,0.0008914087,0.001162562,0.000459145,0.0008670787,0.001245898,0.001379702,0.0006056631],"category_scores_gemma":[0.006493854,0.0002597547,0.0008202267,0.0005542588,0.000625536,0.001101639,0.001163592,0.000850075,0.0002580128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001603479,"about_ca_system_score_gemma":0.001177739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006236854,"about_ca_topic_score_gemma":0.003665107,"domain_scores_codex":[0.9987661,0.0004286051,0.00008531173,0.0003935246,0.000175321,0.0001510381],"domain_scores_gemma":[0.9977869,0.0009101389,0.0002511962,0.000351184,0.0005475359,0.0001530416],"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":[0.001405429,0.00131546,0.04353491,0.0001081252,0.0003477105,0.0002800419,0.0001765511,0.6015356,0.005330839,0.0007414235,0.0014779,0.3437461],"study_design_scores_gemma":[0.00001809389,0.0002058558,0.002067572,0.000009384392,0.00002430832,0.00005973397,0.00002269054,0.9937702,0.003006054,0.000675298,0.0001326409,0.00000815509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7205682,0.0007466555,0.2738209,0.0004372478,0.00007770096,0.0001770551,0.0002866754,0.002751526,0.001133993],"genre_scores_gemma":[0.9495107,0.00007346785,0.04958615,0.00008814755,0.00001751346,0.00006083257,0.0002375698,0.0000195246,0.0004060348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006236854,"threshold_uncertainty_score":0.02351362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05372158880732469,"score_gpt":0.3603125295494699,"score_spread":0.3065909407421452,"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."}}