University Hospital RWTH Aachen
As a supramaximal care provider, the University Hospital RWTH Aachen combines patient-oriented medicine and care, teaching and research at an international level. With 36 specialist clinics, 33 institutes and six interdisciplinary units, the university clinic covers the entire medical spectrum. Outstandingly qualified teams of doctors, nurses and scientists work competently for the health of the patients. The bundling of patient care, research and teaching in a central building offers the best conditions for intensive interdisciplinary exchange and close clinical and scientific networking.
Around 9,000 employees ensure patient-oriented medicine and care according to recognized quality standards. With 1,400 beds, the university hospital treats around 50,000 inpatients and 200,000 outpatients a year.
The Department of Diagnostic and Interventional Radiology at the University Hospital Aachen has a particularly strong focus on imaging of breast cancer with magnetic resonance imaging. The head of the department, Prof. Kuhl, is a world-renowned expert in this area and was among the first to advance the use of magnetic resonance imaging for breast cancer diagnosis.
Role in the project
In ODELIA, UKA acts as one of the scientific coordintors of the project. The Department of Diagnostic and Interventional Radiology at UKA will strongly collaborate with the group by Prof. Kather at TUD in the development of the swarm learning network and the underlying software foundation. Based on this, Aachen will lead the first clinical application scenario – detection of breast cancer in breast MRI – and develop it together with all ODELIA partners.
Daniel Truhn
Prof. Dr. Daniel Truhn is a board-certified radiologist and physicist with a background in image processing. He received his diploma in physics from RWTH Aachen University and his Master in Optics and Photonics from Imperial College, London. After completing his medical studies, he began his residency in the department of Diagnostic and Interventional Radiology in Aachen where he is currently a senior radiologist and the head of the machine learning group. He is university professor at RWTH Aachen University, where he holds the Chair of AI in Medicine. He is the author of numerous high-ranking publications dealing both with the application of AI models to clinical practice as well as with the advancement of AI methodology.
Together with Prof. Jakob Kather from TUD, he leads the ODELIA consortium as scientific coordinator.
Christiane Kuhl
Prof. Dr. Christiane Kuhl is a board-certified radiologist and Director of the Clinic of Diagnostic and Interventional Radiology at University Hospital RWTH Aachen as well as university professor at RWTH Aachen University, where she holds the Chair of Diagnostic and Interventional Radiology. She studied medicine at the University of Bonn, where she later also worked as senior consultant and university professor.
Prof. Kuhl’s scientific focus lies on the image-guided therapy of tumor diseases and breast cancer diagnostics. She has written fundamental, widely cited works that have had a lasting impact on the diagnosis and early detection of breast cancer.
She acts as co-coordinator in the ODELIA consortium.
Gustav Müller-Franzes
Dr. Gustav Müller-Franzes studied electrical engineering at RWTH Aachen University. He developed deep learning methods for the classification of breast cancer when pursuing his bachelor thesis at the Institute of Imaging and Computer Vision (RWTH Aachen University). Following that, he investigated the effects of inter-rater segmentation variance on radiomics features during his master thesis. He joined Prof. Team in 2020 and is now a postdoctoral fellow involved in several projects with the primary goal to establish and validate deep learning methods in clinical practice.
Debora Jutz
Debora Jutz is a medical computer scientist focusing on machine learning classifying breast magnetic resonance images. She studied medical computer science at University Heidelberg and Heilbronn University for the bachelors degree. Debora completed her M.Sc. in medical computer science from the University of Tübingen in 2023, specializing in security. Her master’s thesis focused on implementing inference on a privacy-preserving Convolutional Neural Network (CNN) using secure three-party computation.
Currently, Debora is pursuing a PhD in the research department of radiology at the University Hospital Aachen, under the guidance of Dr. Truhn. Her research centers on improved breast cancer diagnosis in MRI by Deep learning, with the goal of contributing to the advancement of the field of radiology.