In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.
Do Transformers and CNNs Learn Different Concepts of Brain Age?
02
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
03
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
04
Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.
DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions
05
Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.
Design studies for clinical prediction
06
Spanagel R., Bach P., Banaschewski T., et al.
The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives
07
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
08
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
09
Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.
Secure federated learning for Alzheimer's disease detection
10
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
11
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.
Performance reserves in brain-imaging-based phenotype prediction
12
Schulz, M.A., Hetzer, S., Eitel, F., Asseyer, S., Meyer-Arndt, L., Schmitz-Hübsch, T., et al
Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis
13
Vorisek, C., Stellmach, C., Mayer, P., Klopfenstein, S., Bures, D., Diehl, A., Henningsen, M., Ritter, K., Thun, S.
Artificial Intelligence Bias in Health Care: Web-Based Survey
14
Klingenberg, M., Stark, D., Eitel, F. et al