In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Schulz, M. A., Siegel, N. T., & Ritter, K.
Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection
02
Gijsen, S., & Ritter, K.
EEG-Language Modeling for Pathology Detection
03
Reinhardt, P., Zacharias, N., Fislage, M., Böhmer, J., Hollunder, B., Reppmann, Z., ... & Winterer, G.
Machine Learning Classification of Smoking Behaviours-From Social Environment to the Prefrontal Cortex
04
Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.
Do Transformers and CNNs Learn Different Concepts of Brain Age?
05
Gijsen, S., & Ritter, K
Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG
06
Langhammer T, Unterfeld C, Blankenburg F, ..., Ritter K, et al