Unsupervised anomaly detection with deep generative models: an experimental analysis of model variability and mitigation strategies.

Purpose: Unsupervised 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 with identify anomalies. Such approach has drawbacks as the reconstructions are imperfect, and model variability is not taken into account. Approach: We study model variability arising from using different random seeds during training and explore strategies to mitigate the effect of unwanted reconstruction errors and variability. The strategies include model ensembling, anomaly map normalization, and anomaly map designs based on single or multiple pseudo-healthy reconstructions. We compare these approaches in the context of dementia-related anomalies on 3D FDG PET from ADNI using variational autoencoder models. Results: Our experiments suggest that variance between models can be reduced by aggregating their reconstructions in a Conclusion: We highlight a largely overlooked source of variability in deep generative models, their random seed, which can lead to substantially different anomaly detection performance and biased evaluation. Our experimental analysis emphasizes that it is crucial to design robust anomaly maps and to mitigate the impact of randomness-induced variability, by accounting for model reconstruction errors for instance using