  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 ISLES-2015
segmentation benchmarks the ceVAE achieves unsupervised AUROCs of 0.95 and
0.89, respectively, thus outperforming other reported deep-learning based
approaches.
