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:
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!
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.
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.
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.
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.
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.
Distributed deep learning systems can help ascertain data governance and sovereignty while allowing the training of algorithms on larger an more diverse datasets. This is particularly beneficial in the medical domain, in which the collection and curation of large datasets is especially difficult.
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.
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.