SS26 |

AIR — Summer 2026

Mon, 13 Apr 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:

Module: 401040415

Credits: 2 SWS

Programme: Medical Students at Uni Bonn

Language: English

Format: Seminar (2 SWS)

Time: Fridays, 09:00 – 11:00

Location: Online

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
May 15	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.
June 12	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. ,
July 10	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.
	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
	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.
July 17	Student Paper Presentations and Course Wrap-Up	Student presentations.

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

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 — Summer 2026

Mon, 13 Apr 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 (Summer 2026)

Segmentation-Aware Benchmarking of Clinical, Radiomic, Deep-Learning and Foundation-Model Biomarkers for NSCLC Survival Prediction on Pretreatment CT

Can AI support reproducible imaging-biomarker discovery for non-small cell lung cancer (NSCLC)? Students benchmark zero-shot and promptable tumour-segmentation models, clinical risk models, handcrafted radiomics and pretrained CT foundation-model representations for prognosis and phenotype discovery. The central question: do prognostic conclusions change when the tumour contour comes from an automated model instead of an expert?

Dataset: — pretreatment thoracic CT of 422 patients with manual 3D gross-tumour-volume contours and survival data ( ), the cohort of the landmark radiomics study by Aerts et al.
Team: 4 students; all share data handling and phenotyping, each leads one experimental work package.
Software: all experiments run on the lab’s open for consistent pre-processing, metrics and figures.

WP	Topic	Lead
1	Data handling, normalisation, harmonisation and quality control	all
2	Zero-shot / promptable segmentation benchmark (MedSAM, SAM-Med3D, FastSAM3D)	S1
3	Clinical survival model (Cox, random survival forest, calibration)	S2
4	Radiomics (PyRadiomics), feature selection and stability under automated contours	S3
5	Foundation-model benchmarking (CT-CLIP, DINOv2, MedicalNet)	S4
6	Correlation analysis, phenotyping and survival curves	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 .

AiM — Summer 2026

Mon, 23 Mar 2026 00:00:00 +0000

Block Course: AI in Medicine (AiM)

Lecturers:
Tutors: , , , , ,
Module: CM1659
Credits: 2 SWS
Programme: PhD Candidates at BIGC CPS and NeuroScience
Language: English
Format: Block Course (2 SWS)
Time: Tuesday to Thursday, 14-16 April 2026, 09:00 – 17:00
Location: UKB, Venusberg-Campus 1, building C66, 1st floor, seminar room 101

Description

This 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 course is structured around a series of core topics that are central to contemporary AI in medicine. It begins with an introduction to deep learning, providing the conceptual basis for understanding neural networks and their role in medical applications. Building on this foundation, students are introduced to key tasks such as detection and classification, as well as segmentation, which are essential in areas such as diagnostic imaging, image-guided analysis, and automated clinical decision support. The course then expands to quality control in AI systems, with a particular emphasis on uncertainty estimation and interpretability, highlighting how trustworthy and transparent models can be developed and assessed. Further sessions address bias and causality, encouraging students to critically examine fairness, generalizability, and the difference between correlation-driven prediction and clinically meaningful reasoning. Finally, the course introduces vision-language models and their emerging role in connecting medical images, clinical text, and multimodal reasoning.

Each lecture is followed by an interactive lab in which students apply, explore, and critically reflect on the methods discussed. These labs are designed to deepen understanding through hands-on experimentation, collaborative problem-solving, and discussion of real-world medical use cases. In this way, the course not only introduces students to the technical building blocks of AI in medicine but also emphasizes the broader methodological and ethical questions that arise when such systems are developed for healthcare.

By the end of the course, students will have gained a structured overview of important AI methods in medicine, an understanding of their main areas of application, and a critical perspective on their limitations, risks, and clinical potential.

Learning Goals

Technical Skills

Understand and critically appraise current research in AI in Medicine.
Deepen expertise in medical image segmentation, registration, classification, uncertainty quantification, and bias/causality.

Topics and Focus Areas

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

Deep Learning Architectures (U-Nets, GANs, Vision Transformers)
Segmentation & Classification (e.g., anatomical structure delineation, lesion detection)
Uncertainty Quantification & Bias (Bayesian learning, causal inference, dataset shift)

🗓 Block Course Schedule (14-16 April 2026)

Date	Time	Topic	Description
Tuesday, April 14	09:00 – 09:30	Intro
	09:30 – 10:30	Lecture: Detection and Classification	Introduction to SoTA algorithms for Detection and Classification
	11:00 – 12:30	Lecture: Quality Control: Interpretbility	Introduction to SoTA Interpretability algorithms
	14:00 – 17:00	Lab: Detection and Classification	Introduction to SoTA algorithms for Detection and Classification
Wednesday, April 15	09:00 – 10:30	Lecture: Segmentation	Introduction to deep learning concepts specific to medical imaging.
	11:00 – 12:30	Lecture: Quality Control: Uncertiantity	Introduction to uncertainty and quantification methods
	14:00 – 17:00	Lab: Segmentation and Interpretbility	Introduction to SoTA algorithms for Segmentation and Interpretability
Thursday, April 16	09:00 – 10:30	Lecture: Bias and Causality	Introduction to Bias and Causality in Medical Imaging
	11:00 – 12:30	Lecture: Vision-Language Models	Introduction to Vision-Language Models
	14:00 – 17:00	Lab: Segmentation (Cont.)	Free time to complete the labs.

Recommended Prerequisites

At least one of the following:

Familiarity with fundamental medical imaging techniques and an interest in AI applications.
Understanding of medical diagnostics and clinical data interpretation.
A solid background in Python programming, linear algebra, probability, and numerical algorithms is recommended.

References and Resources

[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.

Registration

Registration takes place via or by contacting .