72 citations · 96 across the 18 of their papers we have counts for
7 papers · 1 filter
Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
Aditya Parikh, Eike Petersen, Stella Frank +3
Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representation invariance often being tou…
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…
Prevalence calibration as shortcut mitigation
Mohamed Amine Kina, Eike Petersen
Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim t…
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…
Effect of Demographic Bias on Skin Lesion Classification
Ralf Raumanns, Gerard Schouten, Veronika Cheplygina +1
In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, parti…
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…