{"id":"W2810924486","doi":"10.1016/j.media.2018.07.001","title":"Synthesizing retinal and neuronal images with generative adversarial nets","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":214,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Chinese Government Scholarship; China Scholarship Council; Government of Jiangxi Province","keywords":"Computer science; Artificial intelligence; Annotation; Set (abstract data type); Generative grammar; Image (mathematics); Pattern recognition (psychology); Computer vision","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":[0.0006160713,0.001018921,0.0005087267,0.0004347904,0.0001494839,0.0006020571,0.0008211574,0.0009163413,0.001197257],"category_scores_gemma":[0.001932858,0.0004804209,0.0007603064,0.0002828052,0.0005876841,0.0004705128,0.0008777907,0.001092625,0.0003740981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005490434,"about_ca_system_score_gemma":0.0003851564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001739501,"about_ca_topic_score_gemma":0.002222931,"domain_scores_codex":[0.9997593,0.00005459978,0.000009501121,0.00007148936,0.00007792995,0.00002717548],"domain_scores_gemma":[0.9995064,0.0002878639,0.00005721504,0.00005953702,0.0000576081,0.00003151138],"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":[0.00006832609,0.0000216391,0.0003867747,0.00004811386,0.00003754165,0.0001629586,0.00004172757,0.9366776,0.01092318,0.003879267,0.001057333,0.04669547],"study_design_scores_gemma":[0.000002797641,0.00001188353,0.00004735494,0.00000377276,0.000003836407,0.00003440513,0.000002888392,0.9967092,0.00166924,0.001248133,0.0002628885,0.000003554477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0238058,0.0002484092,0.9729831,0.0001802965,0.0000595729,0.00004938998,0.0000831685,0.000729189,0.001861062],"genre_scores_gemma":[0.6369857,0.0003647923,0.3561619,0.0004396684,0.00008368371,0.0001358987,0.0004966203,0.0003097157,0.005022005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001739501,"threshold_uncertainty_score":0.004005194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007527008097328543,"score_gpt":0.2789910835645772,"score_spread":0.2714640754672487,"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."}}