8 citations · 14 across the 10 of their papers we have counts for
10 papers
Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging
Stefano Woerner, Christian F. Baumgartner
Data scarcity is a major limiting factor for applying modern machine learning techniques to clinical tasks. Although sufficient data exists for some well-studied medical tasks, the…
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions
Jan Nikolas Morshuis, Matthias Hein, Christian F. Baumgartner
Inverse problems, such as accelerated MRI reconstruction, are ill-posed and an infinite amount of possible and plausible solutions exist. This may not only lead to uncertainty in t…
PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration
Leonard Siegert, Paul Fischer, Mattias P. Heinrich +1
Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural…
Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
Paul Fischer, Hannah Willms, Moritz Schneider +3
Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during…
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
Susu Sun, Lisa M. Koch, Christian F. Baumgartner
While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding informat…
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
Paul Fischer, Thomas Küstner, Christian F. Baumgartner
MRI reconstruction techniques based on deep learning have led to unprecedented reconstruction quality especially in highly accelerated settings. However, deep learning techniques a…