10 citations · 31 across the 12 of their papers we have counts for
3 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…
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…