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cs.CV2026
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…
cs.CV2026
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…
cs.CV2025
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…