{"id":"W3133532155","doi":"10.3390/app11052187","title":"Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":30,"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; F. Hoffmann-La Roche; 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; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Benchmark (surveying); Computer science; Pattern recognition (psychology); Anomaly detection; Anomaly (physics); Convolutional neural network; Image (mathematics); Discriminator; Pipeline (software); Deep learning; Neuroimaging; Autoencoder; Medicine; Cartography","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":[0.0007262626,0.000624113,0.0005292131,0.0004954162,0.0001686764,0.0004700856,0.0007698338,0.000746726,0.0005085837],"category_scores_gemma":[0.001325415,0.0002124068,0.0006936854,0.0002743456,0.0005115945,0.0005684006,0.0004746337,0.0009907198,0.0001261731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007362121,"about_ca_system_score_gemma":0.0005287689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004003743,"about_ca_topic_score_gemma":0.003527097,"domain_scores_codex":[0.9997429,0.00006341112,0.00001154024,0.00007508307,0.00006509638,0.00004194226],"domain_scores_gemma":[0.9994851,0.00025084,0.00007971644,0.00005494408,0.0001039908,0.00002533312],"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":[0.0001255297,0.00006041618,0.003716844,0.00003081675,0.00007307139,0.0001573249,0.00003552531,0.9479346,0.004711996,0.003387149,0.0006920606,0.03907463],"study_design_scores_gemma":[8.702312e-7,0.00001403981,0.0002678952,0.000001594962,0.000004820482,0.00002567159,0.000001599915,0.9982612,0.0006432279,0.0006976868,0.0000791597,0.000002156297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2466994,0.0009394394,0.7480032,0.0007653086,0.00008775335,0.00005428657,0.0002132051,0.0007758574,0.002461426],"genre_scores_gemma":[0.9654918,0.0002687718,0.03099057,0.0001195425,0.00003377089,0.0000313675,0.0002294188,0.00002666216,0.002808155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004003743,"threshold_uncertainty_score":0.007960856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04128451734625569,"score_gpt":0.2989317121754314,"score_spread":0.2576471948291756,"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."}}