About Us
The Lab for AI in Medicine at TU Munich develops algorithms and models to improve medicine for patients and healthcare professionals.
Our aim is to develop artificial intelligence (AI) and machine learning (ML) techniques for the analysis and interpretation of biomedical data. The group focuses on pursuing blue-sky research, including:
- AI for the early detection, prediction and diagnosis of diseases
- AI for personalised interventions and therapies
- AI for the identification of new biomarkers and targets for therapy
- Safe, robust and interpretable AI approaches
- Privacy-preserving AI approaches
We have particularly strong interest in the application of imaging and computing technology to improve the understanding brain development (in-utero and ex-utero), to improve the diagnosis and stratification of patients with dementia, stroke and traumatic brain injury as well as for the comprehensive diagnosis and management of patients with cardiovascular disease and cancer.
We are actively recruiting new members!
Research
Projects
People
Director
Daniel Rückert
Professor of Artificial Intelligence in Healthcare and Medicine
Medical Image Computing, Data Science in Medicine, Artifical Intelligence in Medicine
Senior Researchers
Kerstin Hammernik
Research Scientist
Inverse Problems, Machine Learning, MRI, Medical Image Computing
Veronika Zimmer
Research Scientist
Medical Image Computing, Ultrasound Image Analysis, Fetal Image Analysis
Georgios Kaissis
Senior Research Scientist
Privacy-preserving artificial intelligence, Medical image computing, Probabilistic methods
Martin Menten
Research Scientist
Generative modelling, Unsupervised biomarker detection, Retinal imaging
Researchers
Philip Muller
PhD Student
Multi-Modal Learning, Natural Language Processing, Geometric Deep Learning
Alexander Ziller
PhD Student
Artifical Intelligence in Medicine, Secure and Private Artificial Intelligence
Dmitrii Usynin
PhD Student
Artifical Intelligence in Medicine, Secure and Private Artificial Intelligence
Felix Meissen
PhD Student
Anomaly Detection, Transfer Learning, Generative Models, Bayesian Learning
Leonhard Feiner
PhD Student
Machine Learning and Deep Learning, Medical Image Computing, Data Science
Moritz Knolle
Researcher
Medical Image Computing, Probabilistic Deep Learning, Semantic Segmentation, Federated Learning
Tamara Müller
PhD Student
Artificial Intelligence in Medicine, Geometric Deep Learning, Computational Neuroscience
Vasiliki Sideri-Lampretsa
PhD Student
Artifical Intelligence in Medicine, Machine Learning and Deep Learning, Medical Imaging
Collaborators
Henrik von Kleist
Affliated Researcher
Interpretable AI, Uncertainty quantification in ML, Causal inference
Shadi Albarqouni
Affliated Researcher
Interpretable machine learning, Robustness and uncertainty in ML, Federated learning
Recent Publications
The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants
Model-Based and Data-Driven Strategies in Medical Image Computing
Secure, privacy-preserving and federated machine learning in medical imaging
Teaching
MSc Thesis: Deep Natural Language Processing for Radiology Reports
In recent years deep learning has proven quite successful in the field of natural language processing (NLP). However, many of today’s NLP models require very large datasets for training, thus self-supervised pre-training (like BERT [1]) on large unlabeled datasets has become quite important.
Seminar on Federated Learning (SoSe2021)
Master Seminar (IN2107, IN4410) (2 SWS, 5 ECTS) offered for BioMedical Computing (BMC) program at the Chair for Computer Aided Medical Procedures and Augmented Reality, TU Munich Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, Prof.
MSc Thesis: Machine Learning in Fetal MRI Reconstruction
Fetal Magnetic Resonance Imaging (MRI) has become increasingly important to assess the development of the fetal brain. However, the acquisition is challenging due to the uncontrollable fetal motion. This requires both improved MR acquisition and reconstruction procedures.
MSc Thesis: Motion-Compensated MRI Reconstruction
Long acquisition times in Magnetic Resonance Imaging (MRI) bear the risk of patient motion, which substantially degrades the image quality. Further sources of image degradation are physiological motion, such as periodic respiratory and cardiac motion.
MSc Thesis: Physics-Driven Self-Supervised Learning in MRI Reconstruction
Machine learning has evolved tremendously to accelerate the inherently low acquisition process of Magnetic Resonance (MR) images. However, it is challenging to obtain ground truth data for learning MRI reconstruction. The objective of this MSc thesis is to explore self-supervised learning for MRI reconstruction, where only the measurement (k-space) data and knowledge about the acquisition physics are available.
MSc Thesis: Distributionally robust neural networks in medical imaging
Deep learning has revolutionized the field of medical imaging. However, the performance of a model drops when the distribution of the test data is different from the distribution of the training data.
MSc Thesis: Out-of-distribution detection using contrastive training for medical imaging
Deep learning has revolutionized the field of medical imaging. However, the performance of a model drops when the distribution of the test data is different from the distribution of the training data.
MSc Thesis: Privacy-preserving Deep Learning in Medical Imaging
Privacy-preserving artificial intelligence techniques such as differential privacy, encryption and multi-party computation can reconcile the needs for data utilisation and data protection in the medical domain, as mandated by legal and ethical requirements.
Vacancies
We are recruiting team members who would like to join us for a PhD, MSc, BSc or guided research/interdisciplinary project on an ongoing basis! If you’d like to join us, please get in touch using the form below or via e-mail and attach a motivation letter, transcript of academic records and CV.