In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Serin, E., Ritter, K., Schumann, G., Banaschewski, T., Marquand, A., & Walter, H
Generating synthetic task-based brain fingerprints for population neuroscience using deep learning
02
Schulz, M. A., Siegel, N. T., & Ritter, K.
Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection
03
Gijsen, S., & Ritter, K.
EEG-Language Modeling for Pathology Detection
04
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
05
Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.
Do Transformers and CNNs Learn Different Concepts of Brain Age?
06
Gijsen, S., & Ritter, K
Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG
07
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
08
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
09
Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.
DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions
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Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.
Design studies for clinical prediction
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Spanagel R., Bach P., Banaschewski T., et al.