{"id":"W3175008056","doi":"","title":"Collaborative Diabetic Retinopathy Severity Classification of Optical Coherence Tomography Data through Federated Learning","year":2021,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Optical coherence tomography; Diabetic retinopathy; Computer science; Medicine; Optometry; Artificial intelligence; Ophthalmology; Diabetes mellitus; Endocrinology","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":[0.006177756,0.000968955,0.001921154,0.004335148,0.000996564,0.001671143,0.002193835,0.001586374,0.001082611],"category_scores_gemma":[0.01193677,0.0003209999,0.001888835,0.001675111,0.0004788798,0.001775872,0.002911612,0.001784569,0.0007900526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019655,"about_ca_system_score_gemma":0.002264637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331458,"about_ca_topic_score_gemma":0.01125846,"domain_scores_codex":[0.99699,0.0007583317,0.0003689885,0.001002854,0.0005169757,0.0003627606],"domain_scores_gemma":[0.9934296,0.002474743,0.0003760333,0.001415678,0.001839374,0.0004645383],"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":[0.0013863,0.002935203,0.0947483,0.0001401633,0.0007896937,0.0003676283,0.0004315285,0.1022353,0.003936863,0.00120699,0.009036966,0.782785],"study_design_scores_gemma":[0.00007284345,0.0003269038,0.005920809,0.00003380164,0.0001624959,0.0001309641,0.0001670768,0.9840768,0.003510971,0.003904992,0.001656788,0.00003551373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4377311,0.001247745,0.5435432,0.001108254,0.0003511241,0.0005420267,0.003781519,0.009196661,0.002498435],"genre_scores_gemma":[0.8389814,0.0001481653,0.1522431,0.0002627953,0.0001184054,0.0001697725,0.00676657,0.0001004144,0.001209395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01331458,"threshold_uncertainty_score":0.03267145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06230165884786266,"score_gpt":0.3741723239116168,"score_spread":0.3118706650637541,"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."}}