Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies
Publication Details
Journal
SPIRE - Sciences Po Institutional REpository
Published
2026-02-15
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Funders
Foundation for the National Institutes of Health
Alzheimer's Disease Neuroimaging Initiative
Northern California Institute for Research and Education
European Commission
Agence Nationale de la Recherche
Grand Équipement National De Calcul Intensif
National Institutes of Health
HORIZON EUROPE Framework Programme
Canadian Institutes of Health Research
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article
Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies
Journal
SPIRE - Sciences Po Institutional REpository
Topics
Anomaly Detection Techniques and Applications
article
Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies
article en
Abstract
No abstract available for this paper.
Publication Tag
SPIRE - Sciences Po Institutional REpository
Funders
Foundation for the National Institutes of Health, Alzheimer's Disease Neuroimaging Initiative, Northern California Institute for Research and Education, European Commission, Agence Nationale de la Recherche, Grand Équipement National De Calcul Intensif, National Institutes of Health, HORIZON EUROPE Framework Programme, Canadian Institutes of Health Research
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Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies · SPIRE - Sciences Po Institutional REpository (2026) | TGRS Research Map | TGRS