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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…
eess.IV2026
Generative Modeling of Complex-Valued Brain MRI Data
Marco Schlimbach, Moritz Rempe, Jessica Mnischek +4
Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence that it encodes tissue propert…
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…