{"id":"W3161307971","doi":"10.3390/jimaging9030069","title":"GANs for Medical Image Synthesis: An Empirical Study","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":241,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Medical imaging; Computer vision; RGB color model; Pattern recognition (psychology)","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":[0.008566062,0.001075754,0.0005818756,0.0008320228,0.0002556018,0.0008669089,0.0009649005,0.0009708626,0.002520194],"category_scores_gemma":[0.03699797,0.0003819968,0.0007401663,0.0006960479,0.0009815174,0.001371381,0.0007409577,0.001472738,0.0004944835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015444,"about_ca_system_score_gemma":0.0003968882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0030194,"about_ca_topic_score_gemma":0.002520768,"domain_scores_codex":[0.9967473,0.002085579,0.0001274623,0.00042385,0.0005149994,0.0001008709],"domain_scores_gemma":[0.9670943,0.02885952,0.0007969667,0.002079472,0.0009514985,0.0002182708],"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":[0.001290588,0.001027999,0.03721768,0.001538845,0.0007190184,0.0003179841,0.0003218997,0.6487455,0.005229635,0.008911917,0.01635057,0.2783283],"study_design_scores_gemma":[0.000117186,0.0007237476,0.01103112,0.0002151041,0.0001351117,0.0005284917,0.0001220013,0.9707965,0.004846399,0.006003149,0.005444265,0.00003698704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7656686,0.02234694,0.1870067,0.003484773,0.0003690543,0.0007079915,0.002505532,0.002090391,0.01581998],"genre_scores_gemma":[0.9679958,0.002029411,0.02632089,0.0002964879,0.0001014977,0.0001207792,0.001614413,0.0001347504,0.001385851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008566062,"threshold_uncertainty_score":0.04530221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636498055289708,"score_gpt":0.3402729384433184,"score_spread":0.3139079578904213,"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."}}