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Unsupervised learning can leverage large-scale data sources without the needfor annotations. In this context, deep learning-based autoencoders have showngreat potential in detecting anomalies in medical images. However, especiallyVariational Autoencoders (VAEs)often fail to capture the high-level structurein the data. We address these shortcomings by proposing the context-encodingVariational Autoencoder (ceVAE), which improves both, the sample, as well aspixelwise results. In our experiments on the BraTS-2017 and ISLES-2015segmentation benchmarks the ceVAE achieves unsupervised AUROCs of 0.95 and0.89, respectively, thus outperforming other reported deep-learning basedapproaches.

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper