About Us

We aim to develop novel techniques for the identification of new MRI- derived clinically applicable biomarkers for the complex task of personalised prediction of disease activity in MS patients.

We propose using state-of-the-art machine (deep) learning techniques, in particular convolutional neural networks (CNNs), for the purpose of extracting individual multiple sclerosis (MS) disease severity and activity markers from structural magnetic resonance imaging (MRI) data.

Prof. Dr. rer. nat. Kerstin Ritter (P.I.)

Fabian Eitel (PhD Student)

Claudia Chien (Study Coordinator & Post-doctoral Researcher)

Moritz Seiler (PhD Student)

We’re always happy to talk about potential collaborations!

Collaboration is key to success in the clinic, and this project will include many neurologists, neuroradiologists, machine learning experts, clinical researchers, and other interdisciplinary experts working together towards a common goal.



Visualizing evidence for Alzheimer’s disease in deep neural networks trained on structural MRI data

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis onconventional MRI using layer-wise relevance propagation

Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer’s disease classification

Collaborative study in deep learning for predicting disease activity in multiple sclerosis (deepMS)

Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification

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