{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004045101,0.0001081128,0.0002202118,0.0003845834,0.0002748957,0.0001253917,0.0005955242,0.00002635683,0.00002063411],"category_scores_gemma":[0.00001508979,0.0001059895,0.00009665786,0.003454625,0.0001792194,0.0004395023,0.0001906331,0.0000940122,0.000001467827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002029955,"about_ca_system_score_gemma":0.0001916424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001244448,"about_ca_topic_score_gemma":0.00005784696,"domain_scores_codex":[0.9986144,0.00005269578,0.0002708128,0.0005468832,0.0002994059,0.0002157661],"domain_scores_gemma":[0.9992983,0.00003948632,0.0001201713,0.0003664905,0.00007078911,0.0001048093],"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":[0.000006535094,0.0001744036,0.006857781,0.000004098027,0.00005737185,0.00000952449,0.0005108165,0.8885104,0.04326862,0.05299986,0.000002470094,0.007598084],"study_design_scores_gemma":[0.00008888775,0.0000151227,0.007191726,0.000002709187,0.000109914,9.046635e-7,0.0001612349,0.9783376,0.01101206,0.002940809,0.000009306083,0.0001296783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3772787,0.00007092112,0.6215761,0.00006194795,0.00001416514,0.00008087107,0.0000026917,0.00007115242,0.0008434524],"genre_scores_gemma":[0.8559062,0.0000116692,0.1439816,0.00005318025,0.00001043791,0.00001942683,0.000004645858,0.000003457379,0.000009390791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4786275,"threshold_uncertainty_score":0.4322126,"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."}}