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arXiv · 1812.05941

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

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

Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring. This improves the sample- as well as pixel-wise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks, the ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89, respectively, thus outperforming state-of-the-art methods by a considerable margin.

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David Zimmerer, Simon A. A. Kohl, Jens Petersen, Fabian Isensee, Klaus H. Maier-Hein. 2018-12-14. Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection. https://arxiv.org/abs/1812.05941

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