In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.
Secure federated learning for Alzheimer's disease detection
02
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
03
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.
Performance reserves in brain-imaging-based phenotype prediction
04
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
05
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
06
Klingenberg, M., Stark, D., Eitel, F. et al
Higher performance for women than men in MRI-based Alzheimer’s disease detection
07
Wang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S., ... & Alzheimer’s Disease Neuroimaging Initiative
Deep neural network heatmaps capture Alzheimer’s disease patterns reported in a large meta-analysis of neuroimaging studies
08
Brandt, L., Ritter, K., Schneider-Thoma, J., Siafis, S., Montag, C., Ayrilmaz, H. et al
Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis
09
Fast, L., Temuulen, U., Villringer, K., Kufner, A., Ali, H.F., Siebert, E., Huo, S et al
Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke
10
Rane, R. P., Musial, M. P. M., Beck, A., Rapp, M., Schlagenhauf, F., Banaschewski, T., ... & IMAGEN consortium
Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure
11
Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K
Data augmentation via partial nonlinear registration for brain-age prediction
12
Chien, C., Seiler, M., Eitel, F., Schmitz-Hübsch, T., Paul, F., & Ritter, K.
Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity
13
Rane, R. P., de Man, E. F., Kim, J., Görgen, K., Tschorn, M., Rapp, M. A., ... & IMAGEN consortium.
Structural differences in adolescent brains can predict alcohol misuse
14
Subramaniam, P., Kossen, T., Ritter, K., Hennemuth, A., Hildebrand, K., Hilbert, A., ... & Madai, V. I.
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks
15
Rane, R. P., Heinz, A., & Ritter, K
AIM in Alcohol and Drug Dependence
16
Brasanac, J., Ramien, C., Gamradt, S., Taenzer, A., Glau, L., Ritter, K. et al