In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.
Design studies for clinical prediction
02
Spanagel R., Bach P., Banaschewski T., et al.
The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives
03
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
04
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
05
Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.
Secure federated learning for Alzheimer's disease detection
06
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
07
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.