In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
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Hammelrath, L., Rane, R. P., Gijsen, S., Jüres, F., Brose, A., Ritter, K., ... & Knaevelsrud, C.
Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study
02
Gijsen, S., Schulz, M. A., & Ritter, K.
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model.
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Ritter, K., Brandt, L., & Walter, H.
KI in der Psychiatrie
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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.
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Serin, E., Ritter, K., Schumann, G., Banaschewski, T., Marquand, A., & Walter, H
Generating synthetic task-based brain fingerprints for population neuroscience using deep learning
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Schulz, M. A., Siegel, N. T., & Ritter, K.
Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection
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Gijsen, S., & Ritter, K.
EEG-Language Modeling for Pathology Detection
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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
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Langhammer T, Unterfeld C, Blankenburg F, ..., Ritter K, et al