Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies

Abstract

International audienceUnsupervised anomaly detection allows identifying anomalies from unlabeled data, making it useful for neuroimaging analysis and computer-aided diagnosis. Given an individual's scan, we use a generative model to construct a subject-specific image of healthy appearance and compare both images to spot anomalies. Designing anomaly maps in such way has drawbacks as the reconstructions are imperfect and some variability is not taken into account. We study model variability arising from using different random seeds during training and explore solutions to mitigate the effect of unwanted reconstruction errors and variability. Our experiments on 3D brain FDG PET scans from ADNI suggest that variance between models can be reduced by aggregating their reconstructions in a Z-score based anomaly map, or normalizing the anomaly map with a healthy validation set