In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.

01
Sun, S., Koch, L. M., & Baumgartner, C. F.

Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?

Oct 01, 2023 | In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 425-434). Cham: Springer Nature Switzerland.
02
Nazari, P., Damrich, S., Hamprecht, F.A.

Geometric Autoencoders – What You See is What You Decode

Jul 23, 2023 | International Conference on Machine Learning 2023
03
Ayhan, M. S., Faber, H., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.

Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration

Jul 21, 2023 | Translational Vision Science & Technology, 12(4), 12-12
04
Djoumessi, K. R. D., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., Berens, P. & Koch, L. M.

Sparse Activations for Interpretable Disease Grading

Jul 20, 2023 | Proceedings of Medical Imaging with Deep Learning (MIDL)
05
Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner,, C.F.

Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

Jul 20, 2023 | Medical Imaging with Deep Learning 2023
06
Grote, T., & Berens, P

Uncertainty, evidence, and the integration of machine learning into medical practice

Jul 19, 2023 | The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine (Vol. 48, No. 1, pp. 84-97). US: Oxford University Press
07
Congiu, M., Mondoloni, S., Zouridis, I. S., Schmors, L., Lecca, S., Lalive, A. L., Ginggen, K., Deng, F., Berens, P., Paolicelli, R. C., Li, Y., Burgalossi, A. & Mameli, M.

Plasticity of neuronal dynamics in the lateral habenula for cue-punishment associative learning

Jul 12, 2023 | Molecular Psychiatry, 1-10
08
Böhm, J. N., Berens, P., & Kobak, D.

Unsupervised visualization of image datasets using contrastive learning

May 30, 2023 | Proceedings of the International Conference on Learning Representations (ICLR)
09
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.

From t-SNE to UMAP with contrastive learning

May 30, 2023 | Proceedings of the International Conference on Learning Representations (ICLR)
10
Bachmann, F., Hennig, P., Kobak, D.

Wasserstein tSNE

Mar 17, 2023 | European Conference on Machine Learning 2023
11
Janschewski, J., Käppler, C., & Berens, P.

School predictors of mental health problems in children and adolescents based on a survey of students in hospital and regular schools

Jan 10, 2023 | Zeitschrift für Pädagogische Psychologie