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
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
02
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
03
Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.
Performance reserves in brain-imaging-based phenotype prediction
04
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
05
Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K
Data augmentation via partial nonlinear registration for brain-age prediction
06
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
07
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.
A cognitive fingerprint in human random number generation
08
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative
MRI Image Registration Considerably Improves CNN-Based Disease Classification
09
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
10
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
11
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
12
Schulz, M. A., Chapman-Rounds, M., Verma, M., Bzdok, D., & Georgatzis, K.
Inferring disease subtypes from clusters in explanation space
13
Stark, D., & Ritter, K.