5 papers · 1 filter
Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning
Lukas T. Rotkopf, Marco Schlimbach, Julius C. Holzschuh +3
Purpose: To assess whether breast lesion segmentation can be learned directly from acquired MRI k-space, and whether doing so improves robustness when data are accelerated or noisy…
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…
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…
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…
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Fabian Hörst, Moritz Rempe, Helmut Becker +3
Digital Pathology is a cornerstone in the diagnosis and treatment of diseases. A key task in this field is the identification and segmentation of cells in hematoxylin and eosin-sta…