{"id":"W4387398254","doi":"10.21203/rs.3.rs-3399187/v1","title":"Perception-Enhanced Generative Adversarial Network for Synthesizing Tau Positron Emission Tomography images from Structural Magnetic Resonance Images: a cross-center and cross-tracer study","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Genentech; IXICO; National Natural Science Foundation of China; Novartis Pharmaceuticals Corporation; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; Eisai; Alzheimer's Association","keywords":"Positron emission tomography; Center (category theory); Magnetic resonance imaging; Generative adversarial network; Perception; Generative grammar; Nuclear magnetic resonance; Physics; Artificial intelligence; Image (mathematics); Psychology; Computer science; Medicine; Neuroscience; Radiology; Chemistry","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":[0.003124335,0.001383824,0.0008294762,0.0005991563,0.0002388662,0.0007538078,0.0010569,0.001249625,0.00123863],"category_scores_gemma":[0.005434338,0.0004756922,0.0009604462,0.0003684495,0.0006322616,0.0006696308,0.0009716956,0.001691582,0.0003249847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007948763,"about_ca_system_score_gemma":0.0007339813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009591551,"about_ca_topic_score_gemma":0.005396778,"domain_scores_codex":[0.999388,0.0002630004,0.00002473215,0.0001747119,0.00007876203,0.0000707153],"domain_scores_gemma":[0.9973004,0.001968872,0.0001552047,0.0001883248,0.000295435,0.00009184617],"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":[0.0002668199,0.0001143735,0.001855685,0.00006839132,0.000146782,0.0001030544,0.00004588052,0.951044,0.002313214,0.00116741,0.0009098733,0.04196454],"study_design_scores_gemma":[0.000004109681,0.00002499564,0.0001675308,0.000003549361,0.000008383988,0.00001127101,0.000003483712,0.9989071,0.0005112584,0.0002850907,0.00006997763,0.000003274049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2937969,0.003187768,0.6956276,0.0009512947,0.0003487805,0.0001477833,0.0004041861,0.001867707,0.003667996],"genre_scores_gemma":[0.9477714,0.000418183,0.0484309,0.0002695536,0.00007387243,0.00005801803,0.0006778834,0.0001321708,0.002168066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009591551,"threshold_uncertainty_score":0.01907146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05263348607991228,"score_gpt":0.4437288269110339,"score_spread":0.3910953408311216,"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."}}