72 citations · 96 across the 18 of their papers we have counts for
7 papers · 1 filter
Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study
Peachapong Poolpol, Henrik H. J. Detjen, Eike Petersen
Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect a model's true decision proces…
Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Chiara Tappermann, Steffen Renisch, Lars Ole Schwen +3
Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for…
CoMeT: A foundation model for medical image analysis through federated, multidimensional context integration
J. Raphael Schäfer, Kai Geissler, Till Nicke +27
Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and t…
The Impact of Preprocessing Methods on Racial Encoding and Model Robustness in CXR Diagnosis
Dishantkumar Sutariya, Eike Petersen
Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the potential for racial shortcut le…
Robustness and sex differences in skin cancer detection: logistic regression vs CNNs
Nikolette Pedersen, Regitze Sydendal, Andreas Wulff +3
Deep learning has been reported to achieve high performances in the detection of skin cancer, yet many challenges regarding the reproducibility of results and biases remain. This s…
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…
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…