In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
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
02
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
03
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
04
Rane, R. P., Heinz, A., & Ritter, K
AIM in Alcohol and Drug Dependence
05
Brasanac, J., Ramien, C., Gamradt, S., Taenzer, A., Glau, L., Ritter, K. et al
Immune signature of multiple sclerosis-associated depression
06
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
07
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
08
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.
A cognitive fingerprint in human random number generation
09
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
10
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
11
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
12
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
13
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
14
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
15
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
16
Stark, D., & Ritter, K.