Machine Learning in Medical Image Analysis

In February 2021, Dr. Christian Baumgartner joined the Cluster of Excellence 'Machine Learning' at the University of Tübingen as head of the Independent Research Group 'Machine Learning in Medical Image Analysis'.

Research Interests

Our research focuses on developing technologies rooted in machine learning and computer vision with the aim to accurately and efficiently analyse medical imaging data.

ML-powered medical image analysis is poised to revolutionise healthcare by improving the quality and reliability of diagnoses, by personalising treatments and by helping medical practitioners stem the effects of increasingly data heavy medical tests and an ageing population. However, in order to make machine learning part of real-life clinical decision making systems, to create explainable human-in-the-loop models and to take full advantage of the richness of medical data, a more fundamental understanding of the behaviour of machine learning methods must be developed.

In our research, we pursue topics that will help to bridge the gap between clinical applications and machine learning theory. Topics of particular interest include uncertainty estimation, robustness of predictions, working with scarce labels, and using generative modelling to extract knowledge from large medical data collections.

Further Information

For further information see Christian Baumgartner's Website.


After obtaining his PhD from the King’s College London in the School of Biomedical Engineering & Imaging Sciences in 2016, Christian Baumgartner has worked as a post-doc with Prof. Daniel Rueckert in the Biomedical Image Analysis Group at Imperial College London (2016-2017) on deep learning based systems for fetal ultrasound analysis, and as a post-doctoral fellow (funded by the competitive ETH Fellowship) with Prof. Konukoglu in the Biomedical Image Computing Group at ETH Zürich (2017-2019). There his focus shifted to generative modelling and its application to topics such as uncertainty estimation in image segmentation, learning from scarce data, and reconstruction of magnetic resonance images. Before joining the Cluster, he was working as a senior research engineer at PTC Vuforia (2020-2021) with a focus on deep learning and computer vision.


Dr. Christian Baumgartner
Machine Learning in Medical Image Analysis

University Tübingen
Cluster of Excellence 'Machine Learning'
Maria-von-Linden-Str. 6, 4th floor
Room No. 40-7/A3
72076 Tübingen

+49 7071 70847