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
Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) – a study protocol for a multicentre observational study in outpatient clinics
07
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
08
Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.
DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions
09
Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.
Design studies for clinical prediction
10
Spanagel R., Bach P., Banaschewski T., et al.
The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives
11
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
12
Schulz, M.A., Albrecht, J.P., Yilmaz, A., Koch, A., Kainmüller, D., Leser, U. & Ritter, K.