WS2627 |

AIR — Winter 2026/27

Thu, 15 Oct 2026 00:00:00 +0000

Seminar (2 SWS): Artificial Intelligence in Radiology: Applications and Research (Artifizielle Intelligenz in der Radiologie: Anwendungen und Forschung)

Lecturers:

Tutors: to be announced

Time: November 15 – Jan 31 (every Friday, ~~11:30 AM - 1:00 PM~~ 16:00 - 17:30)

Format: Online (Zoom Meeting) with potential face-to-face meetings, if participants interested! Link was provided via email.

Language: English

eCampus: to eCampus

Description

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.

Course Objectives

Upon completing this seminar, students will be able to:

Grasp deep learning concepts and their specific applications in medical imaging.
Analyze advanced image analysis tasks, including classification, detection, segmentation, and disease progression modeling.
Evaluate ethical and regulatory considerations in the deployment of AI within healthcare.
Discuss the future potential of AI-driven solutions in personalized medicine and patient care.

Target Audience

This seminar is designed for medical students at the Faculty of Medicine, University Hospital Bonn, who are keen on leveraging AI technologies to enhance their medical practice, specifically within diagnostic imaging.

Prerequisites and Desired Knowledge

Prerequisites: Familiarity with fundamental medical imaging techniques and an interest in AI applications.
Desired: Understanding of medical diagnostics and clinical data interpretation.

Seminar Topics and Schedule (Tentative)

Date	Topic	Description	Materials
TBA	Deep Learning Foundations	Introduction to deep learning concepts specific to medical imaging.	Meskó, Bertalan, and Marton Görög. “A short guide for medical professionals in the era of artificial intelligence.” NPJ digital medicine 3.1 (2020): 126.
TBA	Detection and Classification in Medical Imaging	Exploring detection and classification techniques in diagnosing diseases from medical images.	Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88. , Chen, Xuxin, et al. “Recent advances and clinical applications of deep learning in medical image analysis.” Medical image analysis 79 (2022): 102444. ,
TBA	Segmentation	Deep learning methods for segmenting key structures in medical images.	Tajbakhsh, Nima, et al. “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.” Medical image analysis 63 (2020):101693.
TBA	Causality and Bias	Causality and Bias in deep learning with medical imaging.	Castro, Daniel C., Ian Walker, and Ben Glocker. “Causality matters in medical imaging.” Nature Communications 11.1 (2020): 3673. , Jones, Charles, et al. “A causal perspective on dataset bias in machine learning for medical imaging.” Nature Machine Intelligence 6.2 (2024): 138-146
TBA	~~Uncertainity Quantification~~	Moved to the next year due to holiday season
TBA	Q&A Session	Students should prepare questions for their assigned papers and discuss them with the tutors
TBA	Uncertainity Quantification	Quanitifying Uncertainty in Deep Learning Models	Huang, Ling, et al. “A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods.” Medical Image Analysis (2024): 103223.
TBA	Student Paper Presentations (1)	Student presentations.
TBA	Student Paper Presentations (2) and Course Wrap-Up	Wrap-up, feedback, and discussion on future AI applications in medical practice.

Learning Format

One of the seminar’s unique teaching concepts is “Teach It to Yourself.” Participants (limited to 20 partisipants) will present selected chapter and papers, engage in discussions, and critically analyze case studies on deep learning applications in medical imaging.

Registration (closed)

Interested in this seminar, please register via email to and

References

[1] Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88.
[2] Shen, Dinggang, Guorong Wu, and Heung-Il Suk. “Deep learning in medical image analysis.” Annual review of biomedical engineering 19.1 (2017): 221-248.
[3] Chen, Xuxin, et al. “Recent advances and clinical applications of deep learning in medical image analysis.” Medical image analysis 79 (2022): 102444.
[4] Tajbakhsh, Nima, et al. “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.” Medical image analysis 63 (2020):101693.
[5] Wang, Ge, Jong Chul Ye, and Bruno De Man. “Deep learning for tomographic image reconstruction.” Nature machine intelligence 2.12 (2020): 737-748.
[6] Ben Yedder, Hanene, Ben Cardoen, and Ghassan Hamarneh. “Deep learning for biomedical image reconstruction: A survey.” Artificial intelligence review 54.1 (2021): 215-251.
[7] Huang, Ling, et al. “A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods.” Medical Image Analysis (2024): 103223.
[8] Castro, Daniel C., Ian Walker, and Ben Glocker. “Causality matters in medical imaging.” Nature Communications 11.1 (2020): 3673.
[9] Jones, Charles, et al. “A causal perspective on dataset bias in machine learning for medical imaging.” Nature Machine Intelligence 6.2 (2024): 138-146.
[10] Thirunavukarasu, Arun James, et al. “Large language models in medicine.” Nature medicine 29.8 (2023): 1930-1940.
[11] Zhang, Jingyi, et al. “Vision-language models for vision tasks: A survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

CoMI-L — Winter 2026/27

Thu, 15 Oct 2026 00:00:00 +0000

Lab: Computational Medical Imaging (CoMI)

Lecturers:
Tutors:
Module: MA-INF 2230
Credits: 4 SWS, 9 CP (270h workload)
Programme: M.Sc. Computer Science
Mode: Optional (2. or 3. Semester)
Language: English
Format: Lab (Praktikum), groups of max. 3 students
Frequency: Every year

Description

The Computational Medical Imaging (CoMI) Lab is a project-based course in which students carry out a semester-long research project in computational medical imaging. Working in small groups, students identify a well-defined problem, collect or are provided a suitable dataset, implement baseline and novel approaches, and evaluate their results both quantitatively and qualitatively.

Possible project topics (chosen or proposed by the instructors) include:

Anatomical Structure Segmentation (e.g., multi-atlas, CNN-based)
Deformable Registration (e.g., classic demons, learning-based registration)
Lesion/Anomaly Detection (e.g., out-of-distribution detection in X-ray)
Radiomics & Quantitative Biomarker Extraction (e.g., texture features, deep radiomics)
Uncertainty Estimation in Deep Learning (e.g., MC-Dropout, Bayesian networks)
Causality & Bias Analysis in Medical Datasets (e.g., domain shift, fairness)

Learning Goals

Technical Skills

Acquire a deep understanding of a selected problem in computational medical imaging: from problem identification to data processing, algorithm design, implementation, and evaluation.
Implement, test, and validate state-of-the-art image-analysis algorithms (e.g., segmentation, registration, reconstruction, classification) on real medical data.
Develop proficiency in using libraries and toolkits commonly used in medical imaging (e.g., ITK, SimpleITK, PyTorch for deep learning, nibabel/dipy).
Learn to document software systematically (API documentation, code comments, unit tests) and to generate reproducible experiments (version control, containerization).

Soft Skills

Collaborate effectively in small groups over the entire semester, dividing tasks (e.g., data curation, algorithmic implementation, evaluation).
Prepare clear, concise technical documentation and design rationales.
Present project progress and final results in oral form (midterm demo, final demo).
Critically assess one’s own results and position them within the state-of-the-art in computational medical imaging.

🗓 Project (Winter 2026/27)

Who Does the Model See? Bias of Ultrasound Foundation Models across Countries and Income Settings in Fetal Ultrasound

Ultrasound is the imaging modality of antenatal care worldwide, and in low- and middle-income countries scans are increasingly done by non-specialists on low-cost devices. Ultrasound foundation models promise label-efficient AI support exactly there — but they are pretrained almost entirely on data from high-income hospitals. Students build a harmonised benchmark from 12 openly licensed fetal-ultrasound releases (13 countries, four World Bank income groups) and test whether frozen foundation models work equally well for fetuses scanned in Malawi, Uganda, Sierra Leone, Ghana, Egypt, Algeria, Brazil and China as in Spain, the Netherlands, Romania and the UK.

Bias metrics: worst-group performance, between-group gaps and calibration, with patient-level confidence intervals.
Clinical highlight: a measurement study — do errors in the cephalic index, abdominal circumference and cardiothoracic ratio, and so the share of fetuses flagged as abnormal, differ by site, device and income group?
Models: FetalCLIP, USFM, SonoNet-64 (USF-MAE secondary).
Team: 3 students; all share data harmonisation and the final synthesis, each leads one experimental work package. Students implement their own toolkit (fusbias) against a provided specification and acceptance tests.

WP	Topic	Lead
1	Data harmonisation, de-duplication, patient-level splits and quality control	all
2	Zero-shot and training-free standard-plane recognition	S1
3	Frozen-feature probing and bias audit	S2
4	Clinical measurements: cephalic index, abdominal circumference, cardiothoracic ratio	S3
5	Representation analysis, synthesis and report	all

Learning Format

Students work in groups of up to 3 on a semester-long project. Each group will:

Identify a well-defined problem and collect or be provided a suitable dataset (publicly available or hospital data under ethics approval).
Perform a literature review to select baseline methods.
Implement baseline and at least one novel or improved approach.
Evaluate quantitatively (e.g., Dice, Hausdorff, MSE, classification accuracy) and qualitatively (expert review where possible).
Document the software (README, API docs) and provide a reproducible environment (Docker/Singularity).
Present midterm progress (oral demonstration + short written summary) and final results (oral + poster or full report).

Assessment

Graded Exam:
Oral presentation (final demo + Q&A, 20 minutes per group). Written report (12–16 pages, including background, methods, results, discussion).

Ungraded Coursework (required for admission to the exam):
Project work; attendance in course sessions in accordance with the exam regulations of 2023, § 12(6).

Recommended Prerequisites

At least one of the following:

MA-INF 2222 – Visual Data Analysis
MA-INF 2312 – Image Acquisition and Analysis in Neuroscience
MA-INF 2220 – Lab Visualization and Medical Image Analysis (or equivalent)

A solid background in Python programming (and/or C++), linear algebra, calculus, probability theory, and numerical algorithms is required. Familiarity with deep learning frameworks is highly 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 .