In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
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
02
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
03
Stark, D., & Ritter, K.
AIM and Gender Aspects
04
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
05
Srivastava, S., Eitel, F., & Ritter, K.
Predicting fluid intelligence in adolescent brain MRI data: An ensemble approach
06
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
07
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
08
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
09
Hornstein, S., Seiler, M., Hoffman, V., Nelson, B., Aschbacher, K., Ritter, K., & Hilbert, K.