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arXiv · 1912.00003

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

David Zimmerer·Jens Petersen·Simon A. A. Kohl·Klaus H. Maier-Hein
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Abstract

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variational Autoencoder based score approximation yield a theoretically better grounded and more faithful estimate. In our experiments, Variational Autoencoder gradient-based rating outperforms other approaches on unsupervised pixel-wise tumor detection on the BraTS-2017 dataset with a ROC-AUC of 0.94.

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David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein. 2019-11-28. A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients. https://arxiv.org/abs/1912.00003

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