105 citations · 151 across the 16 of their papers we have counts for
5 papers · 1 filter
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition
Tobias Weber, Jakob Dexl, David Rügamer +1
We address the computational barrier of deploying advanced deep learning segmentation models in clinical settings by studying the efficacy of network compression through tensor dec…
WindowNet: Learnable Windows for Chest X-ray Classification
Alessandro Wollek, Sardi Hyska, Bastian Sabel +2
Chest X-ray (CXR) images are commonly compressed to a lower resolution and bit depth to reduce their size, potentially altering subtle diagnostic features. Radiologists use windowi…
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image qu…
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we prog…
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
Alessandro Wollek, Theresa Willem, Michael Ingrisch +2
To investigate the impact of OOD radiographs on existing chest X-ray classification models and to increase their robustness against OOD data. The study employed the commonly used c…