{"id":"W3105017984","doi":"10.1007/978-3-030-58589-1_12","title":"EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Curriculum; Glaucoma; Artificial intelligence; CAD; Debiasing; Machine learning; Fundus (uterus); Process (computing); Sensitivity (control systems); Class (philosophy); Categorization; Dual (grammatical number); Ophthalmology; Medicine; Engineering drawing","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":[0.0006051746,0.0005077239,0.0004522451,0.0005949152,0.0002941879,0.0006628847,0.001704107,0.0009580484,0.009328736],"category_scores_gemma":[0.001474893,0.0002234703,0.0004739476,0.0004264709,0.000267529,0.0007345078,0.00195861,0.001202869,0.002210732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006337638,"about_ca_system_score_gemma":0.001151298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006908339,"about_ca_topic_score_gemma":0.01013796,"domain_scores_codex":[0.9997306,0.00005642355,0.00001413397,0.00006464138,0.00009922457,0.00003478519],"domain_scores_gemma":[0.9996576,0.0001251036,0.00001722248,0.00003927721,0.0001174228,0.00004334608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001348865,0.0001365088,0.0009606339,0.00007614623,0.00002840938,0.00008107579,0.0000687514,0.08197912,0.00635406,0.008173413,0.01406424,0.8879428],"study_design_scores_gemma":[0.00002798231,0.00005786271,0.000301855,0.00001903279,0.00001180948,0.00006658483,0.00002761598,0.9742434,0.005535563,0.009568604,0.01012759,0.0000121075],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003247181,0.0001124616,0.9906378,0.0001004673,0.00003306633,0.00005471738,0.000199072,0.004005698,0.001609507],"genre_scores_gemma":[0.08295287,0.0001975205,0.9090075,0.0002094506,0.000039222,0.0002174125,0.0008007245,0.0002784272,0.006296883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009328736,"threshold_uncertainty_score":0.03120768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02487421734547697,"score_gpt":0.2967810849737178,"score_spread":0.2719068676282408,"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."}}