WS2526 |

AIR — Winter 2025/26

Mon, 13 Oct 2025 00:00:00 +0000

Special Version: AI in Radiology – From Pixels to Decisions (AiR-GUC Edition)

Coordinators: (University of Bonn), & (German University in Cairo)
Tutors:
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 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:
German University in Cairo: ,

Supported by the , this course aims to build sustainable academic bridges between Germany and Egypt.

Contact

University of Bonn
– Clinic for Diagnostic and Interventional Radiology, UKB

German University in Cairo (GUC)
,

CoMI-S — Winter 2025/26

Mon, 13 Oct 2025 00:00:00 +0000

Seminar: Computational Medical Imaging (CoMI)

Lecturers:
Module: MA-INF 2318
Credits: 2 SWS, 4 CP (120 h workload)
Programme: M.Sc. Computer Science
Language: English
Format: Seminar (2 SWS)
Time: Thursdays, 14:00 – 16:00
Location: TBD, INF 6+8

Description

The Computational Medical Imaging (CoMI) seminar provides an opportunity for students to explore, understand, and critically evaluate contemporary research in computational medical imaging — from classical algorithms to modern deep learning-based techniques.

Participants analyze recent publications from leading journals and conferences, deepening their understanding of medical image segmentation, registration, classification, uncertainty quantification, and bias/causality in imaging. The seminar emphasizes both technical expertise and scientific communication skills.

Learning Goals

Technical Skills

Understand and critically appraise current research in computational medical imaging.
Deepen expertise in medical image segmentation, registration, classification, uncertainty quantification, and bias/causality.
Extract core contributions from scientific papers and position them in the context of the state-of-the-art.

Soft Skills

Develop competence in independent literature search and paper analysis.
Present complex research effectively (written and oral), with appropriate visual and didactic support.
Engage in critical discussions and defend arguments scientifically.
Practice time management and constructive peer feedback.

Topics and Focus Areas

Seminar topics are drawn from recent publications in top-tier venues and may include:

Segmentation & Classification (e.g., anatomical structure delineation, lesion detection)
Registration & Alignment (e.g., atlas-based or diffeomorphic registration)
Quantitative Imaging & Radiomics (feature extraction, prognostic modeling)
Uncertainty Quantification & Bias (Bayesian learning, causal inference, dataset shift)
Deep Learning Architectures (U-Nets, GANs, Vision Transformers)
Ethical, Regulatory, and Clinical Translation Aspects

🗓 Seminar Schedule (Winter 2025/26)

Date	Theme	Description	Presenter	Key References / Papers
23 Oct 2025	Kick-off & Introduction	Overview of seminar format, expectations, topic assignments.	Course Team	Duncan, J.S. and Ayache, N., 2002. Medical image analysis: Progress over two decades and the challenges ahead. IEEE transactions on pattern analysis and machine intelligence, 22(1), pp.85-106.
30 Oct 2025	Foundations of Computational Medical Imaging	Classical image processing and analysis methods.		Gonzalez & Woods, Digital Image Processing, Ch2-Ch4
06 Nov 2025	Segmentation and Classification	Delineation of anatomical structures and lesion detection.		Gonzalez & Woods, Digital Image Processing, Ch10-Ch12, Litjens et al., Med Image Anal 42 (2017)
13 Nov 2025	Registration and Alignment	Image registration methods: deformable, atlas-based, and deep learning approaches.		Rueckert, D. and Schnabel, J.A., 2010. Medical image registration. In Biomedical image processing (pp. 131-154). Berlin, Heidelberg: Springer Berlin Heidelberg. ; Sotiras et al., IEEE TMI 32(7):1153–1190 (2013) .
20 Nov 2025	Quantitative Imaging & Radiomics	Feature extraction, radiomic biomarkers, and prognostic modeling.		Lambin et al., Eur J Cancer 48 (2012) ; McCague, C., et al. “Introduction to radiomics for a clinical audience.” Clinical Radiology 78.2 (2023): 83-98. .
27 Nov 2025	Deep Learning Architectures I	Convolutional Neural Networks, U-Nets for image segmentation and classification.		Goodfellow, Ian, et al. Deep learning. Vol. 1. No. 2. Cambridge: MIT press, 2016. Ch-9 ; Ronneberger et al., MICCAI 2015 ;
04 Dec 2025	Deep Learning Architectures II	Generative Adversarial Networks, Vision Transformers, and self-supervised learning.		Goodfellow, Ian, et al. Deep learning. Vol. 1. No. 2. Cambridge: MIT press, 2016. Ch-20 ; Goodfellow et al. 2020 , Isola et al., CVPR 2017
11 Dec 2025	Uncertainty Quantification	Probabilistic and non-probabilistic approaches to quantifying model uncertainty.		Huang et al., Med Image Anal (2024) .
18 Dec 2025	Causality and Bias in Medical Imaging	Causal reasoning and dataset bias in deep learning for medical applications.		Castro et al., Nat Commun 11 (2020) ; Mittermaier, Mirja, Marium M. Raza, and Joseph C. Kvedar. “Bias in AI-based models for medical applications: challenges and mitigation strategies.” NPJ Digital Medicine 6.1 (2023): 113. ; Chinta, Sribala Vidyadhari, et al. “AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias.” arXiv preprint arXiv:2407.19655 (2024).
08 Jan 2026	Ethics & Clinical Translation	Regulatory and ethical challenges of AI in healthcare.		Topol E., Nature Med 25 (2019) ; Lekadir, Karim, et al. “FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.” bmj 388 (2025).
15 Jan 2026	Student Paper Presentations I	Student presentations and peer feedback.	Students	—
22 Jan 2026	Student Paper Presentations II	Continuation of student presentations.	Students	—
29 Jan 2026	Student Paper Presentations III	Final student session and discussion.	Students	—
05 Feb 2026	Course Wrap-Up & Outlook	Summary of insights and future directions in computational medical imaging.	Course Team	—

Learning Format

Students independently select and present recent research papers in computational medical imaging, fostering discussion and critical evaluation within the seminar group.

Group Presentation: A group of 2-3 Students will present a topic and raise some discussions among the students. They will be asked to write a written summary as a lecture note on their topic (15-20 pages).
Individual Presentation: Each individual will present a relevant paper of her/his choice at the end of the semester

Recommended Prerequisites

At least one of the following:

MA-INF 2222 – Visual Data Analysis
MA-INF 2312 – Image Acquisition and Analysis in Neuroscience

A solid background in Python programming, linear algebra, probability, and numerical algorithms is recommended.

References and Resources

Relevant Journals:

Nature Machine Intelligence
Medical Image Analysis (MedIA)
IEEE Transactions on Medical Imaging (TMI)
Radiology: AI, European Radiology, IEEE Journal of Biomedical and Health Informatics

Relevant Conferences:

MICCAI (Medical Image Computing & Computer-Assisted Intervention)
CVPR (IEEE/CVF Computer Vision and Pattern Recognition)
MIDL (Medical Imaging with Deep Learning)
ISBI (International Symposium on Biomedical Imaging)
RSNA (Radiological Society of North America Annual Meeting)

Registration

Registration takes place via eCampus or by contacting .