{"id":"W3164110046","doi":"10.1016/j.media.2021.102106","title":"Conditional generation of medical images via disentangled adversarial inference","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Heart Institute; Université de Montréal","funders":"","keywords":"Inference; Computer science; Latent variable; Artificial intelligence; Regularization (linguistics); Machine learning; Adversarial system; Image (mathematics); Unsupervised learning; Variable (mathematics); Pattern recognition (psychology); Mathematics","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":[0.001214062,0.0008153174,0.0006701394,0.0006981279,0.0002275577,0.0007954365,0.001339931,0.001311034,0.002739424],"category_scores_gemma":[0.004106127,0.0008713523,0.001124554,0.0005132459,0.0009502021,0.0008927886,0.001941077,0.002021718,0.0008151919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006959046,"about_ca_system_score_gemma":0.0007130746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00192836,"about_ca_topic_score_gemma":0.002720293,"domain_scores_codex":[0.9995618,0.0001501088,0.00001665784,0.0001060559,0.0001256471,0.00003974837],"domain_scores_gemma":[0.9985359,0.001004165,0.0001223522,0.0001679562,0.000117985,0.00005166471],"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":[0.0001515171,0.00005212962,0.0004674812,0.0001035461,0.00007579984,0.0001623741,0.00006172929,0.8901423,0.008873404,0.02903921,0.002709447,0.06816109],"study_design_scores_gemma":[0.000005683419,0.000009671096,0.00006296857,0.000005123917,0.000005573359,0.00003106866,0.000001875362,0.9918411,0.001141517,0.006569111,0.0003222019,0.00000407372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004443987,0.0001284218,0.9939296,0.0002272871,0.00003105615,0.00002974679,0.00009603206,0.0003771937,0.0007366443],"genre_scores_gemma":[0.5930852,0.0006272193,0.3939231,0.0006395499,0.0001807208,0.0002800915,0.0009954886,0.0004118323,0.009856747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002739424,"threshold_uncertainty_score":0.009164333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862280401033647,"score_gpt":0.2944943717499336,"score_spread":0.2758715677395972,"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."}}