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AI in Radiology – From Pixels to Decisions (joint teaching with GUC)

Wed, 01 Jan 2025 00:00:00 +0000

This joint teaching activity was developed within a BMFTR-funded international program to strengthen academic exchange and capacity building in artificial intelligence for medical imaging. The course combines lectures, hands-on exercises, and project-based learning, enabling students to work on clinically motivated imaging problems using AI methods.

Perspective

The initiative contributes to sustainable academic exchange between Germany and Egypt and provides a basis for continued joint teaching activities, student exchange, and integration of research-led topics into international curricula.

Selected Interdisciplinary Research with Methodological AI Contributions

In addition to my core research in artificial intelligence for medical imaging and clinical applications, I have contributed to a small number of exploratory interdisciplinary projects in materials science and food science. These activities focused on transferring methodological expertise in data-driven modeling and machine learning to neighboring domains and align with the University of Bonn’s Transdisciplinary Research Areas Sustainable Futures, Modelling, and Life and Health. Together, they demonstrate the transferability of AI methodologies and my engagement in transdisciplinary research initiatives without detracting from my primary research focus.

Summer School on Affordable AI (SAAI)

Wed, 01 May 2024 00:00:00 +0000

Open Medical Inference (OMI)

Sun, 01 Jan 2023 00:00:00 +0000

Outcomes

The Open Medical Inference (OMI) initiative is a BMFTR-funded national research infrastructure aimed at improving reproducibility, transparency, and comparability of machine learning methods in medical imaging and clinical AI. Within OMI, I contributed to the development and evaluation of standardized benchmarking protocols, open evaluation pipelines, and reference datasets for medical AI, with a particular focus on robustness, generalization, and fair comparison across institutions and data distributions. These activities complement my research on federated learning and trustworthy AI by addressing methodological gaps in evaluation practices and supporting community-wide standards for medical machine learning research.

Our role

Contributing researcher (Scientific Lead) with responsibility for methodological input on benchmarking design, evaluation protocols, and reproducibility aspects, in close collaboration with national partners.

Perspective

OMI provides a sustained national framework for open and reproducible medical AI research. The experience and infrastructure developed within OMI inform ongoing and future projects on federated learning, multi-center evaluation, and trustworthy AI, and strengthen national coordination across academic and clinical institutions. These activities align with and complement my work on federated learning benchmarks and multi-center evaluation (see also Sections and ).

Modelling Uncertainty in Deep Learning for Medical Applications

Wed, 01 Jan 2020 00:00:00 +0000

Deep Learning has emerged as a leading technology for accomplishing many challenging tasks showing outstanding performance in a broad range of applications in computer vision and medical applications. Despite its success and merit in recent state-of-the-art methods, DL tools still lack in robustness hindering its adoption in medical applications. Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has been successfully introduced to computer vision for better understanding the underlying deep learning models. In this proposal, we investigate modeling the uncertainty for medical applications given the well-known challenges in medical image analysis, namely severe class-imbalance, few amounts of labeled data, domain shift, and noisy annotations.

Collaboration:

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