8 papers
Efficient Complex-Valued Vision Transformers for MRI Classification Directly from k-Space
Moritz Rempe, Lukas T. Rotkopf, Marco Schlimbach +6
Deep learning applications in Magnetic Resonance Imaging (MRI) predominantly operate on reconstructed magnitude images, a process that discards phase information and requires compu…
CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation
Hamza Kalisch, Fabian Hörst, Jens Kleesiek +2
As medical imaging is central to diagnostic processes, automating the generation of radiology reports has become increasingly relevant to assist radiologists with their heavy workl…
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
M. Rempe, F. Hörst, C. Seibold +7
We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Moritz Rempe, Fabian Hörst, Helmut Becker +4
Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligen…
Cracking the PUMA Challenge in 24 Hours with CellViT++ and nnU-Net
Negar Shahamiri, Moritz Rempe, Lukas Heine +2
Automatic tissue segmentation and nuclei detection is an important task in pathology, aiding in biomarker extraction and discovery. The panoptic segmentation of nuclei and tissue i…
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
Lukas Heine, Fabian Hörst, Jana Fragemann +6
In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering pat…