129 citations · 194 across the 17 of their papers we have counts for
10 papers · 1 filter
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
Mareike Thies, Fabian Wagner, Noah Maul +10
Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the proje…
An unobtrusive quality supervision approach for medical image annotation
Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu +2
Image annotation is one essential prior step to enable data-driven algorithms. In medical imaging, having large and reliably annotated data sets is crucial to recognize various dis…
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
Fabian Wagner, Mareike Thies, Laura Pfaff +9
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low…
SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology
Alexander Mühlberg, Paul Ritter, Simon Langer +11
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce t…
Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy
Kai Packhäuser, Sebastian Gündel, Florian Thamm +2
Robust and reliable anonymization of chest radiographs constitutes an essential step before publishing large datasets of such for research purposes. The conventional anonymization…
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Fabian Wagner, Mareike Thies, Felix Denzinger +7
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(…