90 citations · 110 across the 6 of their papers we have counts for
6 papers
Foundation Models in Radiology: What, How, When, Why and Why Not
Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Sarah Müller, Louisa Fay, Lisa M. Koch +3
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. As a result, deep learning models tend to l…
Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imaging
Siying Xu, Kerstin Hammernik, Andreas Lingg +5
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, w…
Attention-aware non-rigid image registration for accelerated MR imaging
Aya Ghoul, Jiazhen Pan, Andreas Lingg +6
Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic imag…
Unlocking Robust Segmentation Across All Age Groups via Continual Learning
Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez +3
Most deep learning models in medical imaging are trained on adult data with unclear performance on pediatric images. In this work, we aim to address this challenge in the context o…
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein +1
Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled -space data. However, these appr…