{"id":"W2997616821","doi":"","title":"Generative Modeling for Retinal Fundus Image Synthesis","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Fundus (uterus); Generative grammar; Image (mathematics); Annotation; Generative adversarial network; Generative model; Deep learning; Residual; Encoder; Pattern recognition (psychology); Computer vision; Algorithm; Medicine; Ophthalmology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009733782,0.0005983605,0.0004823745,0.0005306706,0.0001913521,0.000676632,0.0007507867,0.0007593955,0.002285476],"category_scores_gemma":[0.003114921,0.0005500543,0.0009568507,0.0003377667,0.0007276752,0.0005033197,0.0009706694,0.001188759,0.0004719774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008998309,"about_ca_system_score_gemma":0.0005402839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004410053,"about_ca_topic_score_gemma":0.004300982,"domain_scores_codex":[0.9996275,0.0001360558,0.00001381495,0.00008759792,0.0001012917,0.00003378334],"domain_scores_gemma":[0.9990883,0.0005858751,0.00009266086,0.0001167202,0.00007989877,0.00003651654],"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":[0.0000836882,0.00002641616,0.0007371423,0.00004698065,0.00004751314,0.00009529532,0.00004661389,0.9449804,0.006671174,0.0169188,0.00124155,0.02910432],"study_design_scores_gemma":[0.000003877811,0.000007636449,0.00009469791,0.00000422627,0.000003895826,0.00002823618,0.000002344992,0.9940968,0.0009625347,0.004397441,0.0003943527,0.000004120977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01721222,0.0004474435,0.9793943,0.0003839046,0.00003980553,0.00004544401,0.0002421135,0.000584068,0.001650718],"genre_scores_gemma":[0.8127264,0.0006853805,0.1784394,0.0003848629,0.00008085493,0.0001609831,0.0009044504,0.0003131823,0.006304346],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004410053,"threshold_uncertainty_score":0.008768797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550385286363505,"score_gpt":0.3135633875603596,"score_spread":0.2980595346967245,"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."}}