Author

Eike Petersen

Technical University of Denmark (DTU)

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Open access Sep 2026

Comparing Deep Learning Models for Lung Nodule Detection on Photon-Counting CT Images in a Non-Screening Setting

Background: Comprehensive evaluations of lung nodule detection methods remain limited, particularly regarding data from clinical routine, novel photon-counting CT (PCCT) technology, and the comparison of academic and commercial models. Materials and Methods: This retrospective study collected 1860 routine PCCT scans ac...

L. Thieme, Eike Petersen, K. Geissler et al. · medRxiv · 0 citations

Preprint Aug 2026

CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

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 to either sparse or dense outputs, such as classification or segmentation. Here, we present CoM$^3$eT (Co-representation Multidimensional Mul...

J. Schäfer, Kai Geissler, Till Nicke et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias

It is shown, mathematically and empirically, that enforcing demographic invariance can actually hamper bias mitigation and even create new biases.

Aditya Parikh, Eike Petersen, Stella Frank et al. · 0 citations

#machine learning Preprint Sep 2026

Faithful Faithfulness Evaluations: Challenges&Pitfalls Learned from a Breast MRI Case Study

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 process and may therefore mislead clinicians. We investigate this problem using a Vision Transformer-based breast MRI classifier trained on the OD...

Peachapong Poolpol, Henrik H. J. Detjen, Eike Petersen · 0 citations

#computer vision Preprint Aug 2026

Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI

This work builds a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets and proposes a taxonomy of radiological image anomalies based on human visual perception, enabling fine-grained analysis of AD failure modes, establishing a foundation and practical guidance on scalable unsuper...

C. Tappermann, S. Renisch, L. O. Schwen et al. · 0 citations