Special Version: AI in Radiology – From Pixels to Decisions (AiR-GUC Edition)
AI in Radiology – Joint Teaching Initiative (University of Bonn × GUC)
Special Version: AI in Radiology – From Pixels to Decisions (AiR-GUC Edition)
- Coordinators: Prof. Dr. Shadi Albarqouni (University of Bonn), Dr. Mohammed Salem & Dr. Shereen Moataz Afifi (German University in Cairo)
- Tutors: Adea Nesturi, David Gaviria, Jiajun Zeng
- Duration: October 23, 2025 – February 5, 2026 (every Thursday, 09:00 AM - 11:00 AM)
- Format: Blended Seminar + Project-based Collaboration
- Credits: 4 CP (Bonn) / Elective (GUC)
- Language: English
Course Overview
Artificial Intelligence (AI) is rapidly transforming medical imaging, enhancing clinical workflows and diagnostic accuracy. This special edition of the AiR seminar, titled “AI in Radiology – From Pixels to Decisions,” bridges technical and medical perspectives, offering students a deep dive into the theoretical foundations, applications, and challenges of AI in Radiology.
This joint course brings together:
- Medical students from the University of Bonn
- Computer Science students from the German University in Cairo (GUC)
It is jointly funded under the Joint Teaching Initiative and aims to foster international collaboration, interdisciplinary teamwork, and cross-cultural learning through guided lectures, interactive sessions, and project-based research.
Learning Objectives
By the end of this course, students will:
- Understand fundamental and advanced AI methods applied to medical imaging.
- Analyze academic research papers and translate ideas into practical mini-projects.
- Collaborate across disciplines to integrate technical and clinical insights.
- Present and communicate interdisciplinary work effectively.
- Develop sustainable academic and cultural connections between Germany and Egypt.
Teaching & Learning Format
- Hybrid sessions: alternating between live online sessions and optional in-person meetings.
- Group projects: each team (2 GUC CS students + 1 Uni Bonn medical student) develops an interdisciplinary mini-project throughout the semester.
- Guest lectures: experts from Germany and Egypt provide advanced insights into AI in healthcare.
- Final presentations: joint session showcasing team results and reflections.
Seminar Schedule (Winter 2025/26)
| Date | Topic | Description | Presenter | Key References / Materials |
|---|---|---|---|---|
| 23 Oct 2025 | Kick-off & Orientation | Introduction to the seminar, course goals, and project assignments. | Course Team | — |
| 30 Oct 2025 | Foundations of Deep Learning | Overview of deep learning architectures for image analysis. | ||
| 06 Nov 2025 | Detection & Classification in Medical Imaging | Exploring how deep learning identifies and classifies medical abnormalities. | ||
| 13 Nov 2025 | Segmentation in Medical Imaging | Methods for delineating anatomical structures and lesions. | ||
| 20 Nov 2025 | Bias and Causality in AI Models | Understanding dataset bias and causal reasoning in medical AI. | Castro et al., Nat Commun 11 (2020); | |
| 27 Nov 2025 | Uncertainty Quantification | Quantifying uncertainty and improving trust in AI models. | Huang et al., Med Image Anal (2024). | |
| 04 Dec 2025 | Foundation Models & Vision-Language Systems | Exploring transformers and multimodal AI in radiology. | ||
| 11 Dec 2025 | Ethics & Regulatory Aspects | Legal, ethical, and social challenges of deploying AI in healthcare. | Topol, E. Nature Medicine 25 (2019). | |
| 18 Dec 2025 | Midterm Checkpoint & Group Feedback | Group progress presentations and instructor consultation. | All Teams | — |
| 08 Jan 2026 | Guest Talks (Germany–Egypt) | Expert talks on AI applications in clinical and technical domains. | Invited Speakers | — |
| 15 Jan 2026 | Student Project Presentations I | Group project presentations (part 1). | Teams | — |
| 22 Jan 2026 | Student Project Presentations II | Group project presentations (part 2). | Teams | — |
| 05 Feb 2026 | Joint Closing & Reflection | Course wrap-up, cross-cultural discussion, and feedback. | Course Team | — |
Grading & Evaluation
- GUC Students: Elective course – 100% coursework-based (project & presentation).
- Uni Bonn Students: Non-graded seminar – certificate of completion based on attendance, participation, and project contribution.
Collaboration Framework
This seminar is part of the GUC–Uni Bonn Joint Teaching Collaboration, coordinated by:
- University of Bonn: Prof. Dr. Shadi Albarqouni
- German University in Cairo: Dr. Mohammed Salem, Dr. Shereen Moataz Afifi
Supported by the Joint Teaching Program, this course aims to build sustainable academic bridges between Germany and Egypt.
Contact
University of Bonn
Prof. Dr. Shadi Albarqouni – Clinic for Diagnostic and Interventional Radiology, UKB
German University in Cairo (GUC)
Dr. Mohammed Salem,
Dr. Shereen Moataz Afifi
Adea Nesturi
PhD Candidate
Adea is currently a PhD candidate under the supervision of Prof. Dr. Shadi Albarqouni. She earned her master’s degree in Health Data Science from the University of St Andrews, Scotland, in 2024, where her research focused on the application of deep learning techniques for cancer detection. During her master’s program, she also contributed to the BraTS 2021 challenge, working on the segmentation of brain tumors (gliomas), which provided her with valuable experience in medical image analysis and collaborative research in international competitions.Adea’s research interests are centered on harnessing the power of deep learning to solve real-world medical challenges. She is particularly passionate about developing innovative technologies and AI-driven solutions that can directly improve patient care, enhance diagnostic accuracy, and optimize healthcare outcomes. Through her work at the Albarqouni Lab, she aims to bridge the gap between cutting-edge computational methods and practical applications in clinical settings, advancing the field of medical AI while addressing critical healthcare needs.
David D. Gaviria
PhD Candidate
David is currently a PhD candidate supervised by Prof. Dr. Shadi Albarqouni. He earned his master’s degree in AI from FIB, which is a joint program of UPC, UB, and URV. In 2019, he achieved the top spot in the SIIM-ISIC competition for skin lesion classification and has published his work at VISAPP 2023. David has a keen interest in utilizing technology for the betterment of society and intends to make contributions to AI in the field of medicine through his work at Albarqouni Lab.
Jiajun Zeng
PhD Candidate
Jiajun Zeng is a PhD candidate at the University of Bonn, supervised by Prof. Shadi Albarqouni. He holds an MSc in Biomedical Engineering from Shenzhen University. During his master’s studies, he focused on AI for ultrasound image computing under the supervision of Assoc. Prof. Ruobing Huang and Prof. Dong Ni. His research interests span AI for healthcare, particularly in enhancing radiologists’ workflows through anatomical structure segmentation, disease diagnosis, and treatment support — with a focus on scenarios involving limited annotations and scarce computational resources. Currently, he is exploring efficient vision-language models and multimodal large language models for applications in the healthcare domain.
Pranav Kirti
Visiting Researcher & Fulbright Scholar
Pranav is a visiting researcher from the USA on a Fulbright Study/Research scholarship. He recently earned his bachelor’s degree in Data Science from Washington University in St. Louis, and he hopes to pursue a career in academic medicine. His project is titled Multimodal, Explainable Deep Learning for Rapid and Affordable Diagnosis of Retinal Diseases in LMICs.
