{"id":"W3013851350","doi":"10.1186/s40708-020-00104-2","title":"GAN-based synthetic brain PET image generation","year":2020,"lang":"en","type":"article","venue":"Brain Informatics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; National Institute on Aging; Alzheimer's Association","keywords":"Artificial intelligence; Nuclear medicine; Computer science; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002206685,0.0001102086,0.0001707463,0.00004621806,0.00006919059,0.00004216792,0.0001102447,0.0000424374,0.0003040205],"category_scores_gemma":[0.001015356,0.00009458345,0.00006576726,0.0001930054,0.00008405741,0.0000974135,0.0000166319,0.0001883513,0.0002460055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002703703,"about_ca_system_score_gemma":0.0001103188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003685793,"about_ca_topic_score_gemma":2.897284e-7,"domain_scores_codex":[0.9990734,0.0000174096,0.0003975013,0.00008674704,0.0002571735,0.0001677582],"domain_scores_gemma":[0.9991767,0.000124697,0.00009970277,0.0002762917,0.00007291984,0.0002497174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000119798,0.00006148826,0.00004257725,0.0003063374,0.00001222509,0.00001312749,0.0007547851,0.00005756418,0.04302839,0.001370833,0.9497058,0.004634852],"study_design_scores_gemma":[0.0005778933,0.0001164725,0.0000453674,0.00005968921,0.00002872384,0.00003999553,0.0001228023,0.7714707,0.01584898,0.00007652627,0.2114861,0.000126777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0385209,0.000009514403,0.5484423,0.3975815,0.0000583639,0.0007768308,0.00002838757,0.0005831241,0.013999],"genre_scores_gemma":[0.6034615,0.000006062728,0.2515563,0.1439016,0.0003105204,0.00005213581,0.0002970821,0.00002818492,0.0003865304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7714131,"threshold_uncertainty_score":0.3857001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032133113595816,"score_gpt":0.3037085090617631,"score_spread":0.2715753954659471,"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."}}