XAIRAD is a research cooperation between the Clinic for Diagnostic and Interventional Radiology at Ulm University Medical Center and the Visual Computing Group at Ulm University. for AI in medical imaging.
Group Lead
Prof. Dr. med. Meinrad Beer
Director of the Clinic for Diagnostic & Interventional Radiology, Ulm University Medical Center
Profile
sekretariat.radiologie1@uniklinik-ulm.de
Prof. Dr. rer-nat. habil. Timo Ropinski
Head of the Visual Computing Research Group, Ulm University
Profile
timo.ropinski@uni-ulm.de
Jun.-Prof. Dr.-Ing. Michael Götz
Head of the Section Experimental Radiology, Ulm University Medical Center
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michael.goetz@uni-ulm.de
Dr. med. Catharina Lisson
Senior Physician, Clinic for Diagnostic & Interventional Radiology, Ulm University Medical Center
Profile
catharina.lisson@uniklinik-ulm.de
Medical Researchers
Prof. Dr. med. Stefan Schmidt
Deputy Director of the Clinic for Diagnostic & Interventional Radiology, Ulm University Hospital
Prof. Dr. Dr. med. Nico Sollmann
Senior Physician Diagn. & interv. Radiology, Ulm University Hospital
Dr. med. Daniel Vogele
Senior Physician, Clinic for Diagnostic & Interventional Radiology, Ulm University Hospital
Computer Science Researchers
Hannah Kniesel
PhD Candidate, Visual Computing Research Group, Ulm University
Reserach Focus: Deep Learning for Biomedical Image Processing
Daniel Wolf
PhD Candidate, Visual Computing Research Group, Ulm University
Focus: Self-Supervised Pre-Training and Vision-Language Models for Medical Imaging
Luisa Gallée
PhD Candidate, Experimental Radiology, Ulm University Medical Center
Focus: Explainable AI (Capsule Neural Network)
Heiko Hillenhagen
PhD Candidate, Experimental Radiology, Ulm University Medical Center
Focus: PET/CT and PET/MRI Images; Vision-Language Models for Medical Imaging
Yiheng Xiong
PhD Candidate, Experimental Radiology, Ulm University Medical Center
Focus: Unsupervised Domain Adaptation for Medical Imaging
Michael Glöckler
PhD Candidate, Visual Computing Research Group, Ulm University
Focus: Unsupervised Domain Adaptation for Medical Imaging
Sabitha Manoj
Research assistant, Experimental Radiology, Ulm University Medical Center
Focus: Machine Learning Radiomics and Deep Learning for Medical Imaging
Physics Researchers
Dr. rer. nat. Arthur Wunderlich
Diplomphysiker
Research
We conduct research at the intersection of Artificial Intelligence and Medical Imaging, with key focus areas including:
- Explainable AI
- Self-Supervised Pre-Training
- Vision-Language Models and Multimodal Large Language Models
- Radiomics
Our work spans a range of medical imaging modalities, such as:
- CT
- MRI
- PET/CT and PET/MRI
We develop AI solutions for tasks including:
- Classification
- Segmentation
- Visual Question Answering
Our vision is to advance medical imaging and diagnostics by developing, analyzing, and refining AI methods. We aim to make these technologies transparent, robust, and clinically meaningful while critically evaluating their risks and limitations to ensure safe integration into medical practice.
Please find our publications on the following pages:
Contact
Prof. Dr. med. Meinrad Beer
sekretariat.radiologie1@uniklinik-ulm.de
Prof. Dr. rer-nat. habil. Timo Ropinski
Jun.-Prof. Dr.-Ing. Michael Götz
Dr. med. Catharina Lisson
catharina.lisson@uniklinik-ulm.de
Please feel free to contact us!
Image thanks to Gerd Altmann