text: 439,27,1075059407258554369,3491609373,David Zimmerer,Excited to share our new paper on Unsupervised Anomaly Detection 😁 @MIC_DKFZ. Able to simultaneously detect and localize anomalies in medical images and achieves new unsupervised SoTA performance on two public segmentation datasets 😉. Check it out:  <LINK> <LINK>,https://arxiv.org/abs/1812.05941,"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. ","Context-encoding Variational Autoencoder for Unsupervised Anomaly