{"id":"W4390112814","doi":"10.1007/s13721-023-00438-x","title":"Unraveling the complexity: deep learning for imbalanced retinal lesion detection and multi-disease identification","year":2023,"lang":"en","type":"article","venue":"Network Modeling Analysis in Health Informatics and Bioinformatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Pattern recognition (psychology); Metric (unit); Object detection; Machine learning; Task (project management); Cross entropy; Identification (biology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003296281,0.0001832827,0.0004903556,0.0005541679,0.0007750799,0.0001668935,0.00007843784,0.00007162648,9.751378e-7],"category_scores_gemma":[0.0002606874,0.0001356765,0.0001533807,0.001700127,0.00009118706,0.0001645223,0.00006461454,0.0003349297,0.000003118865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006861003,"about_ca_system_score_gemma":0.00005395417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009723731,"about_ca_topic_score_gemma":0.00009103937,"domain_scores_codex":[0.9974364,0.00006592616,0.001608518,0.000141862,0.0003099801,0.0004373188],"domain_scores_gemma":[0.99863,0.000152608,0.0005916057,0.0002419146,0.000180045,0.0002038313],"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":[0.00007727714,0.00001244966,0.01654069,0.001007954,0.0001366931,4.220163e-7,0.002396385,0.9105123,0.000002840347,0.00007029955,0.00001185349,0.06923085],"study_design_scores_gemma":[0.0005771392,0.00005923482,0.00850777,0.0002548915,0.0004910972,0.000004864135,0.003811224,0.9858187,0.000001301171,0.0002299143,0.00010854,0.000135274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1890278,0.0004331154,0.8090528,0.0008872112,0.00004585143,0.000440396,0.000003537269,0.00009273578,0.00001647835],"genre_scores_gemma":[0.9442025,0.003834929,0.05108042,0.000450106,0.00007394968,0.00003361615,0.0002683515,0.00001261763,0.00004351403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7579724,"threshold_uncertainty_score":0.5961366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07675928286089935,"score_gpt":0.3470624879553608,"score_spread":0.2703032050944615,"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."}}