88 citations · 187 across the 11 of their papers we have counts for
14 papers
Cluster-Based Autoencoders for Volumetric Point Clouds
Stephan Antholzer, Martin Berger, Tobias Hell
Autoencoders allow to reconstruct a given input from a small set of parameters. However, the input size is often limited due to computational costs. We therefore propose a clusteri…
Discretization of learned NETT regularization for solving inverse problems
Stephan Antholzer, Markus Haltmeier
Deep learning based reconstruction methods deliver outstanding results for solving inverse problems and are therefore becoming increasingly important. A recently invented class of…
Learned backprojection for sparse and limited view photoacoustic tomography
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Filtered backprojection (FBP) is an efficient and popular class of tomographic image reconstruction methods. In photoacoustic tomography, these algorithms are based on theoreticall…
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Nadja Gruber, Stephan Antholzer, Werner Jaschke +2
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of…
NETT Regularization for Compressed Sensing Photoacoustic Tomography
Stephan Antholzer, Johannens Schwab, Johannes Bauer-Marschallinger +2
We discuss several methods for image reconstruction in compressed sensing photoacoustic tomography (CS-PAT). In particular, we apply the deep learning method of [H. Li, J. Schwab,…
Deep learning versus -minimization for compressed sensing photoacoustic tomography
Stephan Antholzer, Johannes Schwab, Markus Haltmeier
We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particula…