Back to Search
Start Over
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper
- 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]
- Subjects :
- Electrical Engineering and Systems Science - Image and Video Processing
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1907.12258
- Document Type :
- Working Paper