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

Authors :
Zimmerer, David
Kohl, Simon
Petersen, Jens
Isensee, Fabian
Maier-Hein, Klaus
Publication Year :
2019

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 ISLES-2015 segmentation benchmarks the ceVAE achieves unsupervised AUROCs of 0.95 and 0.89, respectively, thus outperforming other reported deep-learning based approaches.<br />Comment: MIDL 2019 [arXiv:1907.08612]

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1907.12258
Document Type :
Working Paper