David and Malek attended the OMI Plenary Meeting 2026 in Erlangen, where the consortium came together to share progress across all work packages, with discussion on the gateway progress, and align on the next steps for AI deployment in medical imaging.
The Computational Imaging Research (CoMI) Lab at the University of Bonn is a project-based lab where students carry out a semester-long research project in computational medical imaging, implementing and evaluating state-of-the-art algorithms on real medical data.
This AiM block course offers an intensive introduction to artificial intelligence in medicine, with a focus on modern deep learning methods for biomedical and clinical applications. Designed as an interdisciplinary format, the course combines concept-driven lectures with interactive labs, allowing students to engage both with the theoretical foundations of AI and with practical challenges in medical data analysis.
The Computational Imaging Research (CoMI) Seminar at the University of Bonn explores current research in Computational Medical Imaging, including classical algorithms and deep learning-based approaches. Students critically analyze state-of-the-art research and develop both technical and presentation skills.
A special joint seminar between the University of Bonn and the German University in Cairo (GUC), delivered under the Joint Teaching Initiative. This interdisciplinary course explores AI in Radiology from both technical and clinical perspectives, combining computational medical imaging and deep learning to foster cross-cultural collaboration, innovation, and sustainable academic exchange.
Our lab is proud to contribute to the organization of MICCAI'25. Prof. Dr. Shadi Albarqouni serves as a member of the Outreach Committee, and Dr. Elodie Germani will serve an Area Chair at MICCAI'25
Abstract. In the last decade, several studies suggested that physical fitness may positively influence brain and cardiovascular health. Brain health is usually assessed through structural and functional imaging techniques to extract biomarkers of aging that can be used to predict brain age ( Dunås et al.