Welcome

We are the Machine Learning in Medical Image Analysis Group at the Cluster of Excellence - Machine Learning: New Perspectives for Science, University of Tübingen

Machine Learning in Medical Image Analysis

Machine Learning in Medical Image Analysis

Bridging the gap between AI and clinical practice

Cluster of Excellence: Machine Learning - New Perspectives for Science

University of Tübingen

Our Research

In the field of medical image analysis, the ultimate goal is to improve patient outcomes. Machine learning can help to achieve this goal by

  • Accelerating and simplifying the analysis of medical images through (partial) automation of diagnosis, outcome prediction, image quantification, and image reconstruction.
  • Developing technology which enables completely novel clinical workflows which are not possible without AI support.
  • Extraction of new clinical knowledge from large image databases, which can inform future clinical decisions, treatments and drug trials.

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:

  • Robustness, Safety and Uncertainty
  • Interpretable Machine Learning
  • Human-in-the-Loop Machine Learning Systems
  • Generative Modelling on Big Medical Datasets

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.

Team

Find a list of the MLMIA alumni here.

Group Leader

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Christian Baumgartner

Independent Research Group Leader

PhD Students

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Jaivardhan Kapoor

PhD student

Probabilistic inference in spatio-temporal models

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Nikolas Morshuis

PhD student

Robust MRI analysis and reconstruction using physics-informed networks

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Paul Fischer

PhD student

Uncertainty quantification in medical prediction systems

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Stefano Woerner

PhD student

Few-shot and meta-learning for learning from few data

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Susu Sun

PhD student

Interpretable Machine Learning, Incorporation of Prior Domain Knowledge into Deep Neural Networks

Master Students

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Alexander Frotscher

Master thesis student

Generative Modeling for Knowledge Discovery

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Anna Wundram

Research project student

Uncertainty estimation in glaucoma diagnosis

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Dustin Theobald

Master thesis student

Explainable AI

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Hannah Willms

Research project student

Uncertainty estimation in dose prediction for radiotherapy

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Leonard Siegert

Master thesis student

Uncertainty estimation in medical image registration

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Mehrin Azan

Research project student

Explainable AI

Other Researchers

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Amel Abdelraheem

Research fellow

Few-shot and meta learning in medical image analysis

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David Jakobs

Medical doctoral student

Optimal clinical human-AI collaboration, (co-supervision with Prof. Dr. med. Sergios Gatidis)

Research Assistants / HiWi’s

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Arthur Jaques

Hiwi

Meta-learning for medical image analysis

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Bartłomiej Baranowski

Hiwi

Meta-learning for medical image analysis

Research Areas

Robustness, Safety and Uncertainty
How can we build robust systems that know when they don’t know?
Robustness, Safety and Uncertainty
Interpretable Machine Learning
How can we build systems that can explain themselves?
Interpretable Machine Learning
Meta-Learning for Diverse Medical Imaging Tasks
How can we build systems that learn to learn from very few data?
Meta-Learning for Diverse Medical Imaging Tasks
Human-in-the-Loop Machine Learning Systems
How can we integrate humans in the training and deployment of ML?
Human-in-the-Loop Machine Learning Systems
Generative Modelling on Big Medical Datasets
How can we extract new clinical knowledge from medical images?
Generative Modelling on Big Medical Datasets

Contact

  • +49 7071 29-70847
  • Maria-von-Linden-Straße 6, AI Research Building, R. 40-5/A4, Tübingen, Baden-Württemberg 72076