Segmentation and Diagnosis
Deep learning pipelines for anatomy-aware segmentation, MRI reconstruction, skin-lesion analysis, and biomedical datasets.
Hochschule Koblenz · RheinAhrCampus
Applied AI, computational science, and physics-aware learning.
We work on machine learning methods for medical imaging, computer vision, scientific modelling, and product-unit neural networks, connecting theoretical structure with practical systems.
Research Areas
Current work spans medical image analysis, complex-valued neural networks, product units, scientific AI, and robust vision systems.
Product-Unit Networks
Neural architectures that combine additive and multiplicative structure for extrapolation, physical modelling, and shape tasks.
Physics-Informed Learning
Machine learning for concentration fields, nuclear masses, nonlinear classification, and interpretable scientific systems.
Professors
The group is led by Prof. Dr. Babette Dellen and Prof. Dr. Uwe Jaekel, with research activity across computer vision, computational intelligence, medical technology, and mathematical modelling.
Current Team
Student researchers include Osamah Sufyan, Martin Brückmann, and Ziyuan Li. Osamah's CV, background, and webpage material are now available from the Team page.