In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
02
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
03
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
04
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
05
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
06
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
07
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
08
Stark, D., & Ritter, K.
AIM and Gender Aspects
09
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
10
Srivastava, S., Eitel, F., & Ritter, K.
Predicting fluid intelligence in adolescent brain MRI data: An ensemble approach
11
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
12
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
13
Böhle, M., Eitel, F., Weygandt, M., & Ritter, K.
Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification
14
Hornstein, S., Seiler, M., Hoffman, V., Nelson, B., Aschbacher, K., Ritter, K., & Hilbert, K.