In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Gijsen, S., Schulz, M. A., & Ritter, K.
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model.
02
Ritter, K., Brandt, L., & Walter, H.
KI in der Psychiatrie
03
Heinrichs, B., Diegelmann, D., Friedrich, O., Heinrichs, J. H., Kellmeyer, P., Madai, V. I., ... & Schleidgen, S.
Neuroethik–eine Bestandsaufnahme und ein Blick in die Zukunft.
04
Serin, E., Ritter, K., Schumann, G., Banaschewski, T., Marquand, A., & Walter, H
Generating synthetic task-based brain fingerprints for population neuroscience using deep learning
05
Schulz, M. A., Siegel, N. T., & Ritter, K.
Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection
06
Gijsen, S., & Ritter, K.
EEG-Language Modeling for Pathology Detection
07
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
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Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.
Do Transformers and CNNs Learn Different Concepts of Brain Age?
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Seiler, M., & Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
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Gijsen, S., & Ritter, K
Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG
11
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
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Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.