{"id":"W4390873488","doi":"10.59275/j.melba.2024-b87a","title":"Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET","year":2024,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Agence Nationale de la Recherche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Grand Équipement National De Calcul Intensif; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Autoencoder; Artificial intelligence; Computer science; Generative model; Anomaly detection; Deep learning; Pattern recognition (psychology); Ground truth; Anomaly (physics); Image (mathematics); Generative grammar; Computer vision","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":[0.003753421,0.001157546,0.0006569445,0.000827322,0.0002169034,0.0008274166,0.0009762131,0.001641152,0.0009125359],"category_scores_gemma":[0.009054468,0.0003909049,0.0009026229,0.0003335754,0.0009211698,0.0006002028,0.001026023,0.0009119857,0.0002366274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008655182,"about_ca_system_score_gemma":0.0007486294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005635804,"about_ca_topic_score_gemma":0.003990821,"domain_scores_codex":[0.9990906,0.0004654888,0.00005119222,0.0001511359,0.000163812,0.00007783403],"domain_scores_gemma":[0.9966615,0.002375855,0.0001787871,0.0003279456,0.0003243445,0.0001315573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007130292,0.0001928153,0.004703229,0.0002481116,0.0002579797,0.0002898445,0.0001269171,0.907967,0.01447374,0.002632191,0.001114997,0.06728014],"study_design_scores_gemma":[0.00001275762,0.00008486119,0.0005317327,0.00001257828,0.00001459422,0.0001026355,0.00001251984,0.9922113,0.006020315,0.000767824,0.0002189425,0.000009872459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.473193,0.002124942,0.5169575,0.001145207,0.0001767228,0.0002378535,0.0007156447,0.003049877,0.002399222],"genre_scores_gemma":[0.8851135,0.0005038008,0.1116247,0.0002423179,0.00003227011,0.00006531516,0.001053487,0.0002789499,0.001085647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005635804,"threshold_uncertainty_score":0.01985025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622369334265318,"score_gpt":0.2913899809458296,"score_spread":0.2751662876031765,"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."}}