171 citations · 267 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner +2
Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or…
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks d…
Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples
Martin Paulikat, Christian M. Schürch, Christian F. Baumgartner
Understanding the interactions of different cell types inside the immune tumor microenvironment (iTME) is crucial for the development of immunotherapy treatments as well as for pre…
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution. In this work, we propose t…