1.2k citations · 1.5k across the 6 of their papers we have counts for
6 papers
Towards Scenario- and Capability-Driven Dataset Development and Evaluation: An Approach in the Context of Mapless Automated Driving
Felix Grün, Marcus Nolte, Markus Maurer
The foundational role of datasets in defining the capabilities of deep learning models has led to their rapid proliferation. At the same time, published research focusing on the pr…
Invariance to Quantile Selection in Distributional Continuous Control
Felix Grün, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action do…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün +17
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision su…
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis +7
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work…
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Felix Grün, Christian Rupprecht, Nassir Navab +1
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging…