In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K
Data augmentation via partial nonlinear registration for brain-age prediction
02
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
03
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
04
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
05
Rane, R. P., Heinz, A., & Ritter, K
AIM in Alcohol and Drug Dependence
06
Brasanac, J., Ramien, C., Gamradt, S., Taenzer, A., Glau, L., Ritter, K. et al
Immune signature of multiple sclerosis-associated depression
07
Kübler, D., Wellmann, S. K., Kaminski, J., Skowronek, C., Schneider, G. H., Neumann, W. J., ... & Kühn, A.