In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
02
Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.
Design studies for clinical prediction
03
Spanagel R., Bach P., Banaschewski T., et al.
The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives
04
Noteboom, S., Seiler, M., Chien, C., Rane, R. P., Barkhof, F., Strijbis, E.M.M,...& Ritter, K.
Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis
05
Schulz, M.A., Albrecht, J.P., Yilmaz, A., Koch, A., Kainmüller, D., Leser, U. & Ritter, K.
TLIMB-a transfer learning framework for image analysis of the brain
06
Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.
Secure federated learning for Alzheimer's disease detection
07
Oliveira, M., Wilming, R., Clark, B., Budding, C., Eitel, F., Ritter, K., Haufe, S.
Benchmarking the influence of pre-training on explanation performance in MR image classification
08
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.