{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005679916,0.0002451527,0.0006882161,0.0003998464,0.0002233838,0.00009788508,0.0001343435,0.00008900345,0.002047432],"category_scores_gemma":[0.0008481909,0.0001664785,0.0002582573,0.001208471,0.00114935,0.0001543218,0.00008352619,0.0003441422,0.0000532244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002898989,"about_ca_system_score_gemma":0.0001284481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002497398,"about_ca_topic_score_gemma":0.00005716708,"domain_scores_codex":[0.9975199,0.000164107,0.0003438257,0.0005911402,0.001004293,0.0003767596],"domain_scores_gemma":[0.9985039,0.000170848,0.0001188968,0.0003448608,0.0003280109,0.0005334881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003985856,0.001411516,0.6746113,0.0002909878,0.03037152,0.009852442,0.002875102,0.00003326619,0.09119135,0.0002408398,0.03184532,0.1532905],"study_design_scores_gemma":[0.01100939,0.004857241,0.4430304,0.00107756,0.09240039,0.0022178,0.002792263,0.327174,0.0916795,0.0002736388,0.02078563,0.002702103],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8664334,0.0003571291,0.09935425,0.02295301,0.00006807947,0.0001631577,0.00001308118,0.0001551728,0.01050271],"genre_scores_gemma":[0.9806896,0.00009931054,0.01485429,0.002246696,0.0008310125,0.000007784129,0.00004125201,0.00002557037,0.001204486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3271408,"threshold_uncertainty_score":0.9988648,"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."}}