Teaching and education are an integral part of our institute's mission. All of our courses—which are heavily influenced by our research—are taught in English. We offer lectures for students from various disciplines, but our core lectures are aimed at computer science students:
Winter semester 2024/25 (WS24/25)
- Practical:
Applied Deep Learning in Medicine (IN2106, IN4314)
- Lecture:
Künstliche Intelligenz in der Medizin I (IN2403)
- Seminar:
Multi-modal AI for Medicine (IN2107)
Trustworthy AI for Medicine (IN2107, IN45048)
Summer semester 2025 (SS25)
- Practical:
Applied Deep Learning in Medicine (IN2106, IN4314)
- Lecture:
Artificial Intelligence in Medicine II (IN2408)
Multimodal Deep Learning (CIT423003)
- Seminar:
Implicit Neural Representation and Neural Fields (IN2107)
Deep Learning for Inverse Problems in Medical Imaging (IN2107)
In addition to the main lecture series, the individual sub-areas and subject areas are explored in depth through practical sessions and seminars. Through direct interaction with our lecturers, students can deepen and apply their acquired knowledge.
As an elective for medical students, we offer:
Computer Science for Medical Students
This course offers students exciting insights into the world of AI methods (especially neural networks) and their applications in medicine. In addition to acquiring basic theoretical knowledge, they gain initial practical experience with Python programming and have the opportunity to train their own neural networks.
In addition to lectures, seminars, and internships, we offer a range of bachelor's and master's projects. You can find our current calls for open projects here:
Unsupervised 3D Clustering and Quantification of Pathological Hotspots in Orthopaedic SPECT/CT Imaging
This project aims to develop an unsupervised learning pipeline for the automatic detection and quantification of abnormal uptake regions ("hotspots") in 3D SPECT/CT scans used in orthopaedic diagnostics. Unlike traditional methods that rely on manual interpretation or fixed thresholds, the model will extract clinically relevant features—such as volume, intensity, and signal variation—to enable more objective and reproducible assessments. Read more
Multimodal Learning from Pre- and Postoperative Imaging for Orthopedic Surgery
This project aims to enhance orthopedic surgical planning and assessment—particularly for knee arthroplasty—by leveraging machine learning models. Radiographs, though widely used, often require expert interpretation, leading to variability across clinicians and institutions. The project addresses challenges in implant sizing and device identification by using real-world multimodal data and tackling issues like uncertainty and bias. By jointly analyzing pre- and postoperative radiographs with modern ML techniques, the goal is to support more accurate, consistent, and automated clinical decision-making. Read more
Early Alzheimer’s Disease Prediction Using Multimodal Longitudinal Data
Alzheimer’s Disease remains a devastating and incurable condition, where early prediction can make a meaningful difference in patient care. This thesis explores a novel direction by moving beyond static baseline data and tapping into the power of longitudinal multimodal information—spanning clinical and MRI measurements—from the ADNI dataset. By capturing temporal patterns over time, it seeks to not only forecast the likelihood of developing Alzheimer’s within a five-year window but also estimate when the disease might begin. Read more
Semi-Synthetic Datasets for Radiological Findings Localization on Chest X-Rays
The role focuses on advancing the use of text- and mask-conditioned diffusion models to generate synthetic medical images, particularly chest X-rays (CXR), to solve data scarcity in medical AI. The candidate will first study existing diffusion-based image generation and editing techniques, then work on improving realism and reducing artifacts in the current synthetic image pipeline. The work addresses key challenges in acquiring annotated medical data, such as privacy, domain expertise, and time constraints. Read more
Generating 3D Brain Tumor Images from Tumor Concentrations using Diffusion Models
Leveraging the power of diffusion models, this thesis explores the generation of high-fidelity 3D brain tumor images from abstract tumor concentration data. By conditioning generative AI on meaningful medical representations, we aim to synthesize realistic MRI-like scans, advancing disease progression studies and privacy-preserving synthetic datasets. Read more
LLM/VLM-based AI Agents Workflow for Simplifying Medical Image Analysis
This project, in collaboration with the University of Oxford, aims to simplify medical image analysis by using LLM/VLM-based agent systems. These models reduce the need for deep learning expertise, automating pipeline development for tasks like cardiac MRI analysis. The project will evaluate the performance of different LLM/VLM backends based on success rates, cost-effectiveness, and prompt clarity. Read more
Medical Image Segmentation Topics
This thesis delves into advanced medical image segmentation, focusing on soft segmentation, label masking, resolution optimization, and point-of-interest prediction. It aims to enhance segmentation accuracy, detect mislabels through gradient analysis, and explore model extrapolation for unseen structures. Read more
Differential Privacy for Interpretability
The research explores using Differential Privacy (DP) to understand hierarchical concept learning in machine learning models and enhance interpretability. It aims to identify learning patterns under DP constraints, develop metrics for concept acquisition, and assess how privacy levels affect learning granularity. Methods include Python implementation, qualitative analysis, and quantitative metric development. Read more
Multi-modal Longitudinal Heterogeneous Aging in UK Biobank
This thesis aims to enhance age prediction by combining imaging and non-imaging biomarkers. By training models on a healthy cohort, it seeks to identify accelerated and decelerated aging. The plan includes a literature review, developing multi-modal models with MRI and radiomics, and analyzing organ-specific aging patterns and lifestyle factors over time. Read more
Physics-based deep learning for hyperspectral neuronavigation
Hyperspectral imaging (HSI) analyzes the electromagnetic spectrum to detect physical and biochemical properties. In the HyperProbe project, the goal is to develop an AI-powered imaging system for brain tumor surgery, using HSI to identify biomarkers of healthy and tumor tissue. Read more