1 citations · 1 across the 5 of their papers we have counts for
5 papers
Unsupervised Physics-Informed Deep Learning for Dual-Energy CT Material Decomposition
Laura Hellwege, Johann Christopher Engster, Moritz Schaar +2
Dual-energy computed tomography (DECT) enables material-specific imaging through acquisitions at two different X-ray energy spectra. Material decomposition from DECT data is an ill…
The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment
Solveig Thrun, Stine Hansen, Zijun Sun +8
Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can be personalized, especially for h…
Unsupervised Deep Learning for Inverse Problems in Computed Tomography
Laura Hellwege, Johann Christopher Engster, Moritz Schaar +2
Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available. To emulate this, in this study we assume it is unkno…
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Solveig Thrun, Stine Hansen, Zijun Sun +7
Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for hi…
Instance Segmentation XXL-CT Challenge of a Historic Airplane
Roland Gruber, Johann Christopher Engster, Markus Michen +4
Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentatio…