Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

David Zimmerer, Simon Kohl, Jens Petersen, Fabian Isensee, Klaus Maier-Hein

Abstract: Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detecting anomalies in medical images. However, especially Variational Autoencoders (VAEs) often fail to capture the high-level structure in the data. We address these shortcomings by proposing the context-encoding Variational Autoencoder (ceVAE), which improves both, the sample- as well as pixelwise results. In our experiments on the BraTS-2017 and ISLES2015 segmentation benchmarks, the ceVAE achieves unsupervised AUROCs of 0.95 and 0.89, respectively, thus outperforming other reported deep-learning based approaches.

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