8 citations · 12 across the 20 of their papers we have counts for
4 papers · 1 filter
Intersectional Disentangling of Temporal and Acquisition Bias in Fetal Ultrasound
Aya Elgebaly, Joris Fournel, Benjamin Laine Jønch Jurgensen +5
Fairness studies of medical imaging AI often explain subgroup performance gaps through under-representation in the training data. We show that intersectional analysis can disentang…
Graph Counterfactual Explainable AI via Latent Space Traversal
Andreas Abildtrup Hansen, Paraskevas Pegios, Anna Calissano +1
Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these mo…
Counterfactual Explanations via Riemannian Latent Space Traversal
Paraskevas Pegios, Aasa Feragen, Andreas Abildtrup Hansen +1
The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions. Counterfactual explanations form a powerful tool for…
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Vincent Olesen, Nina Weng, Aasa Feragen +1
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical…