Seminar: Computational Medical Imaging (CoMI)
Image created with assistance from DALL·E for the CoMI Seminar at the University of Bonn.
Seminar: Computational Medical Imaging (CoMI)
- Lecturers: Prof. Dr. Shadi Albarqouni
- Tutors: Adea Nesturi, David Gaviria, Jiajun Zeng
- 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. PDF |
| 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. PDF; Sotiras et al., IEEE TMI 32(7):1153–1190 (2013) PDF. | |
| 20 Nov 2025 | Quantitative Imaging & Radiomics | Feature extraction, radiomic biomarkers, and prognostic modeling. | Lambin et al., Eur J Cancer 48 (2012) PDF; McCague, C., et al. “Introduction to radiomics for a clinical audience.” Clinical Radiology 78.2 (2023): 83-98. PDF. | |
| 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 PDF; Ronneberger et al., MICCAI 2015 PDF; | |
| 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 PDF; Goodfellow et al. 2020 PDF, Isola et al., CVPR 2017 PDF | |
| 11 Dec 2025 | Uncertainty Quantification | Probabilistic and non-probabilistic approaches to quantifying model uncertainty. | Huang et al., Med Image Anal (2024) PDF. | |
| 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) PDF; 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. PDF; Chinta, Sribala Vidyadhari, et al. “AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias.” arXiv preprint arXiv:2407.19655 (2024). PDF | |
| 08 Jan 2026 | Ethics & Clinical Translation | Regulatory and ethical challenges of AI in healthcare. | Topol E., Nature Med 25 (2019) PDF; Lekadir, Karim, et al. “FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.” bmj 388 (2025). PDF | |
| 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 Prof. Dr. Shadi Albarqouni.
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.
