In the field of medical image analysis, the ultimate goal is to improve patient outcomes. Machine leanring can help to achieve this goal by
Even though tremendous progress has been made for all of those points in research settings, surprisingly little of this technology has made it into medical practice. One reason for this is that the medical domain is an extremely high-stakes application field with extraordinary demands on robustness of algorithms. Another is that algorithmic outputs are not suitable for clinical decision making if neither the patient nor the doctor can understand the reasoning behind the prediction, and clinicians are loath to use the thus-far predominately black-box technology. Both of the above points also have important implications for the certification of AI technology.
Therefore, in order to start harnessing the massive potential of machine learning for healthcare, and to actually use it to improve real patient outcomes, the Machine Learning in Medical Image Analysis group aims to do research that helps to bridge this gap between machine learning and clinical practice. We perform this research along four broad directions:
These topics are described in more detail in the research areas section below.
We are part of the Cluster of Excellence: Machine Learning - New Perspectives for Science and the University of Tübingen.
The group is headed by Dr. Christian Baumgartner.
Probabilistic inference in spatio-temporal models
Robust MRI analysis and reconstruction using physics-informed networks
Uncertainty quantification in medical prediction systems
Few-shot and meta-learning for learning from few data
Extracting biomarkers for cancer immunotherapy response from CODEX histopathology data
Interpretable Machine Learning, Incorporation of Prior Domain Knowledge into Deep Neural Networks
Optimal clinical human-AI collaboration, (co-supervision with Prof. Dr. med. Sergios Gatidis)
Active and reinforcement learning for image segmentation
Radiation therapy dose estimation using deep neural networks