{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002608241,0.0001276284,0.0002918371,0.0002340014,0.0001701437,0.0002707458,0.0009692506,0.00002870369,0.00003051663],"category_scores_gemma":[0.001195173,0.00009715685,0.0001618662,0.0004292087,0.00004545142,0.001156646,0.0001523119,0.0001904259,0.00001651281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003248924,"about_ca_system_score_gemma":0.0001851868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004995974,"about_ca_topic_score_gemma":0.000004762804,"domain_scores_codex":[0.9981177,0.000239307,0.0004880101,0.0002249981,0.0006155444,0.0003144229],"domain_scores_gemma":[0.9983526,0.0006088951,0.0002145789,0.0002744918,0.0003036256,0.0002458024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001791658,0.002205069,0.04770386,0.00005603879,0.0005329603,0.003449365,0.01096751,0.002751067,0.01189718,0.0003803427,0.1560543,0.7638232],"study_design_scores_gemma":[0.001472687,0.0005327592,0.02241778,0.0001225463,0.00009854556,0.0003105608,0.003073813,0.959762,0.004121965,0.001841322,0.00586255,0.0003835117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03897286,0.00006826413,0.9498896,0.009902433,0.0007852921,0.0001495094,0.000001520145,0.00006926539,0.0001612605],"genre_scores_gemma":[0.9483442,0.00001711686,0.05015316,0.0005183935,0.000905249,0.00001053492,3.245635e-7,0.00001723792,0.00003375653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9570109,"threshold_uncertainty_score":0.3961941,"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."}}