In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Gijsen, S., & Ritter, K.
EEG-Language Modeling for Pathology Detection
02
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
03
Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.
Do Transformers and CNNs Learn Different Concepts of Brain Age?
04
Gijsen, S., & Ritter, K
Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG
05
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
06
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
07
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
09
Spanagel R., Bach P., Banaschewski T., et al.
The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives
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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
11
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
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Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.
Secure federated learning for Alzheimer's disease detection
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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
14
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.