{"id":"W4390430383","doi":"10.18280/ts.400614","title":"Advancing Diabetic Retinopathy Severity Classification Through Stacked Generalization in Ensemble Deep Learning Models","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Generalization; Diabetic retinopathy; Ensemble learning; Artificial intelligence; Deep learning; Computer science; Pattern recognition (psychology); Machine learning; Medicine; Diabetes mellitus; Mathematics; Endocrinology","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":[],"consensus_categories":[],"category_scores_codex":[0.000560855,0.0001443375,0.0002611049,0.0001949449,0.000111718,0.00003375518,0.00006135333,0.00005311065,0.00009581119],"category_scores_gemma":[0.00006312256,0.0001406219,0.000082962,0.0007623994,0.00003215658,0.0002345175,0.00002305533,0.0001962142,0.00004184289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362609,"about_ca_system_score_gemma":0.00003472778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008335306,"about_ca_topic_score_gemma":0.00001893223,"domain_scores_codex":[0.9984501,0.0001452487,0.0003817066,0.0003247692,0.0003650413,0.0003331276],"domain_scores_gemma":[0.9995254,0.00005151681,0.0001112909,0.0001416058,0.00009758532,0.00007258273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003027838,0.0004815856,0.1887317,0.0004936171,0.0001526468,0.0001694115,0.01035431,0.3169997,0.4155253,0.001603257,0.0009598343,0.06422575],"study_design_scores_gemma":[0.0009042475,0.0001061564,0.03593243,0.000139654,0.00009735125,0.000005210167,0.001060005,0.9565147,0.00355579,0.0009947823,0.0005253137,0.0001643245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9152367,0.0000943272,0.08179232,0.001168128,0.00004039889,0.0002212897,0.000001451448,0.0001865991,0.001258765],"genre_scores_gemma":[0.9966879,0.0002133923,0.001899913,0.0002318095,0.0001009165,0.00003891448,0.0002548966,0.00002603879,0.0005461795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.639515,"threshold_uncertainty_score":0.5734394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03767551959348139,"score_gpt":0.2873823232650298,"score_spread":0.2497068036715484,"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."}}