116 citations · 298 across the 7 of their papers we have counts for
3 papers · 1 filter
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
David Zimmerer, Jens Petersen, Simon A. A. Kohl +1
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based ano…
Deep Probabilistic Modeling of Glioma Growth
Jens Petersen, Paul F. Jäger, Fabian Isensee +8
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the ass…
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper
David Zimmerer, Simon Kohl, Jens Petersen +2
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 detect…