In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
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
02
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.
A cognitive fingerprint in human random number generation
03
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
04
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
05
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
06
Ritter, M., Ott, D. V., Paul, F., Haynes, J. D., & Ritter, K.