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.
The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on *Artificial Intelligence in Radiology: Applications and Research.* This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.
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 at Universitätsklinikum Bonn, and The University of Bonn in cooperation with the Lebanese American University, University of Tunis El Manar, and Duhok Polytechnic University are delighted to announce the upcoming BILD Summer School in Tunis, 1-5 September 2025.
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.
Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with lymph node metastasis (LNM) being a critical determinant in patient prognosis and therapeutic planning [1-2]. Conventional methods for detecting LNM in PDAC primarily rely on contrast-enhanced CT scans, but these often fall short in sensitivity, especially in early-stage disease.
The Computational Imaging Research (Albarqouni Lab) at the Clinic of Diagnostic and Interventional Radiology at the University Hospital Bonn is pleased to present a seminar on *Artificial Intelligence in Radiology: Applications and Research.* This cutting-edge seminar, spanning two semester hours per week (2 SWS), is specially curated for medical students to explore the transformative role of deep learning in medical imaging, with a focus on tasks such as classification, detection, segmentation, reconstruction, tracking, and disease progression.
Abstract. Conjunctival Melanocytic Intraepithelial Lesions (CMIL) are a significant precursor to conjunctival melanoma, a rare but potentially fatal ocular cancer. The histopathological classification of CMIL is crucial for early diagnosis and treatment planning.