{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004606871,0.0005942165,0.0003626252,0.0003540654,0.0001019766,0.0003474322,0.000678218,0.000591747,0.002083155],"category_scores_gemma":[0.001165235,0.0002905952,0.0005312291,0.0002773241,0.0003497469,0.0002847315,0.0004562279,0.0006970795,0.0004054978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003711111,"about_ca_system_score_gemma":0.000322601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001298864,"about_ca_topic_score_gemma":0.001426827,"domain_scores_codex":[0.99986,0.00003896365,0.000005436649,0.00004114663,0.00003715576,0.00001715885],"domain_scores_gemma":[0.9996659,0.0001908666,0.00002807279,0.00004195883,0.00005410089,0.00001908121],"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":[0.0002980928,0.00008115403,0.001597471,0.000134509,0.00007451787,0.000466424,0.0000702668,0.884091,0.02128296,0.01058307,0.008854535,0.07246603],"study_design_scores_gemma":[0.000008389779,0.00002195999,0.0001884541,0.000004691005,0.000006116724,0.0001270951,0.00000384303,0.992101,0.003681673,0.00273328,0.001117317,0.000006155733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05466598,0.0005761422,0.9357427,0.0005556962,0.0001828206,0.0001712769,0.0008676479,0.002168616,0.005069043],"genre_scores_gemma":[0.7918866,0.0004664255,0.1990498,0.0004705275,0.00007794852,0.0003367953,0.002318002,0.0003846293,0.005009131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002083155,"threshold_uncertainty_score":0.006968856,"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."}}