Hi there, I’m Eike.

I am a Senior Scienstist working in Medical AI at Fraunhofer Institute for Digital Medicine MEVIS and a guest researcher at the radiology department of Hannover Medical School (MHH).

In my research, I aim to contribute to the robustness, fairness, and overall trustworthiness of AI and machine learning models in medicine. This encompasses questions such as:

• Do models perform equally well in different patient populations - and if not, what is the underlying reason for this performance disparity and how can we make models better?
• Are models truly diagnosing the health and disease of patients, or do they merely appear to do so - e.g., by relying on shortcuts?
• Is it possible for models to be agnostic (invariant) to patient demographics - and is this actually desirable?

While I mostly work in deep learning-based medical image analysis, my interests also span the broader medical domain, including, e.g., EHR and time series data.

I am also a co-organizer of the FAIMI initiative for the fairness of AI in medical imaging. We regularly organize workshops and other events - if you want to keep up to date with what we’re doing, you should sign up for our newsletter or join our Slack community!

Finally, I am also involved in the AI Act’s implementation in terms of harmonized technical standards. Specifically, I am contributing to the development of a technical standard on bias assessment and mitigation in CEN/CENELEC JTC21, first via the Dutch NGO AlgorithmAudit and now via NoLeFa-84.

Recent news

• Markus Wenzel heroically stood in for me (I was sick) and single-handedly held our jointly prepared workshop on “AI-based Medical Imaging: Fairness in Models and Causality in Images” at SPIE Medical Imaging 2025. (Feb ‘25)
• My Ph.D. thesis was awarded the Bernd Fischer award for the best MINT thesis at Uni Lübeck. (Dec ‘24)
• Nina Weng presented our work on slice discovery and failure mode discovery as well as the connection between shortcut learning and demographic biases at our MICCAI 2024 FAIMI workshop. (Oct ‘24)
• My amazing collaborators from DTU have presented our (well, primarily their!) work on Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation as an Oral at ECCV 2024. (Sep ‘24)
• A really new chapter has begun! I have become the father of beautiful twins. (Apr ‘24)
• A new chapter has begun! I moved from DTU to Fraunhofer Institute for Digital Medicine MEVIS. (I am also co-affiliated with the radiology department of Hannover Medical School (MHH).) Thank you for a wonderful time at DTU and for a very warm welcome at MEVIS! (Feb ‘24)
• I have joined AlgorithmAudit as a member of their AI Act Standardization team and will represent them in CEN/CENELEC JTC 21, contributing to the development of a harmonized standard for bias assessment and mitigation. (Dec ‘23)
• I am co-organizing two exciting events on fair AI in medical imaging this fall, a workshop at MICCAI and a free, online symposium in November. Be sure to join in if you’re interested in these topics! We have an exciting line-up for both. (Sep ‘23)
• Our Cell Press Patterns perspective piece The Path Toward Equal Performance in Medical Machine Learning is out! Find a short summary here. (July ‘23)
• Presenting our work on risk score fairness and metric choices at the very inspiring FAccT’23 conference in Chicago! Also, we have published a preprint on the fairness of demographic invariance in medical imaging and a PNAS commentary on what to do when complete bias removal is not an option (May/June ‘23).
• Got invited to participate as a speaker in a wonderful Masterclass at the Lorentz center in Leiden on the clinical implementation of surface EMG measurements of the respiratory muscles (April ‘23).
• Went to MICCAI in Singapore to present our work on the impact of dataset group representation on MRI-based AD prediction performance (Sep), co-organized two events on fairness and responsibility in medical ML (1, 2, in Oct), and presented in a wonderful session on Biases in ML at the inaugural Danish Data Science conference (Nov ‘22).
• Two new journal papers with my previous group from Lübeck are out: Blind source separation of inspiration and expiration in respiratory sEMG signals and Model-based Estimation of Inspiratory Effort using Surface EMG. Also, I am now officially affiliated with the Pioneer centre for AI. (Jul ‘22)
• Our survey paper on responsible and regulatory ML for medicine got accepted and published, our MICCAI paper on feature robustness and sex differences in brain MRI got accepted, I wrote about climate change and AI, and we won funding by the DDSA for organizing a workshop on responsible ML for healthcare in autumn. (May/June ‘22)
• Had the honor of co-organizing (together with the amazing Laura Alessandretti) a workshop on Ethical, Secure, and Just AI at the opening event for the new Pioneer Centre for AI. We had an amazing list of speakers and panelists! (Mar ‘22)
• Our paper about surface EMG-based quantification of respiratory effort got published in Critical Care (Dec ‘21)
• Co-organizing a recurring seminar series on responsible AI now! Accessible via Zoom, everyone welcome. :-) (Dec ‘21)
• Started as a postdoc at DTU Compute with Aasa Feragen and Melanie Ganz (Sep ‘21)

Previously, I was a postdoctoral researcher at DTU Compute, in the section for Visual Computing. Within the project Bias and Fairness in Medicine, I (together with my amazing collaborators) worked on the fairness of risk score models in medicine.

Pre-Previously, I worked at the University of Lübeck, in the Institute for Electrical Engineering in Medicine. In a project executed together with the research unit of Dräger Medical, we worked hard to bring surface electromyographic monitoring of respiratory effort into clinical practice for improving mechanical ventilation. My research in this context spanned mathematical modeling, signal processing, parameter identification & statistical inference, all related to either surface electromyographic measurements, respiration, or both. See my previous publications for some of the work we did.