In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
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
02
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
03
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.
A cognitive fingerprint in human random number generation
04
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
05
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
06
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
07
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
08
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
09
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
10
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
11
Stark, D., & Ritter, K.
AIM and Gender Aspects
12
Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification
13
Srivastava, S., Eitel, F., & Ritter, K.
Predicting fluid intelligence in adolescent brain MRI data: An ensemble approach
14
Eitel, F., Soehler, E., Bellmann-Strobl, J., Brandt, A. U., Ruprecht, K., Giess, R. M., ... & Ritter, K.
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
15
Weygandt, M., Behrens, J., Brasanac, J., Söder, E., Meyer-Arndt, L., Wakonig, K., ... & Paul, F.
Neural mechanisms of perceptual decision-making and their link to neuropsychiatric symptoms in multiple sclerosis
16
Böhle, M., Eitel, F., Weygandt, M., & Ritter, K.