10 citations · 21 across the 4 of their papers we have counts for
4 papers
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Alexander Herold, Daniel Sobotka, Lucian Beer +12
Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis…
Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training
Daniel Sobotka, Alexander Herold, Matthias Perkonigg +5
Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodelling, associated with a wide spectrum of diffuse liver d…
Identifying Signatures of Image Phenotypes to Track Treatment Response in Liver Disease
Matthias Perkonigg, Nina Bastati, Ahmed Ba-Ssalamah +6
Quantifiable image patterns associated with disease progression and treatment response are critical tools for guiding individual treatment, and for developing novel therapies. Here…
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Lucas Fidon, Michael Aertsen, Thomas Deprest +14
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distribut…