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.
Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease
08
Eitel, F., Albrecht, J. P., Weygandt, M., Paul, F., & Ritter, K.
Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data
09
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.
A cognitive fingerprint in human random number generation
10
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
11
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
12
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
13
Ritter, M., Ott, D. V., Paul, F., Haynes, J. D., & Ritter, K.
COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
14
Wakonig, K., Eitel, F., Ritter, K., Hetzer, S., Schmitz-Hübsch, T., Bellmann-Strobl, J., ... & Weygandt, M.
Altered coupling of psychological relaxation and regional volume of brain reward areas in multiple sclerosis
15
Schulz, M. A., Yeo, B. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., ... & Bzdok, D.
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
16
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.