72 citations · 96 across the 18 of their papers we have counts for
5 papers · 1 filter
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Nina Weng, Paraskevas Pegios, Eike Petersen +2
Shortcut learning is when a model -- e.g. a cardiac disease classifier -- exploits correlations between the target label and a spurious shortcut feature, e.g. a pacemaker, to predi…
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
Nina Weng, Siavash Bigdeli, Eike Petersen +1
While many studies have assessed the fairness of AI algorithms in the medical field, the causes of differences in prediction performance are often unknown. This lack of knowledge a…
Are demographically invariant models and representations in medical imaging fair?
Eike Petersen, Enzo Ferrante, Melanie Ganz +1
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their pote…
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
Kilian Zepf, Eike Petersen, Jes Frellsen +1
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, i…
On (assessing) the fairness of risk score models
Eike Petersen, Melanie Ganz, Sune Hannibal Holm +1
Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fa…