Machine Learning / Translational Barriers
We explore challenges in applying machine learning to neuroscience and psychiatry, focusing on explainability, data noise, confounders, and generalizability to enhance clinical applications.
01
Seiler, M., Ritter, K.
Pioneering new paths: the role of generative modelling in neurological disease research
02
Noteboom, S., Seiler, M., Chien, C., Rane, R. P., Barkhof, F., Strijbis, E.M.M,...& Ritter, K.
Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis
03
Schulz, M.A., Albrecht, J.P., Yilmaz, A., Koch, A., Kainmüller, D., Leser, U. & Ritter, K.
TLIMB-a transfer learning framework for image analysis of the brain
04
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.
Performance reserves in brain-imaging-based phenotype prediction
05
Schulz, M.A., Hetzer, S., Eitel, F., Asseyer, S., Meyer-Arndt, L., Schmitz-Hübsch, T., et al
Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis
06
Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K
Data augmentation via partial nonlinear registration for brain-age prediction
07
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
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
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
13
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
14
Stark, D., & Ritter, K.