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

01
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
02
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
03
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
04
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)
05
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)
06
Bachmann, F., Hennig, P., Kobak, D.

Wasserstein tSNE

Mar 17, 2023 | European Conference on Machine Learning 2023
07
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
08
Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.

Sparse visual counterfactual explanations in image space

Sep 09, 2022 | DAGM German Conference on Pattern Recognition (pp. 133-148)
09
Müller, S., Koch, L. M., Lensch, H., & Berens, P.

A Generative Model Reveals the Influence of Patient Attributes on Fundus Images

May 09, 2022 | In Medical Imaging with Deep Learning.
10
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in plain sight: Subgroup shifts escape ood detection

Feb 28, 2022 | Proceedings of Medical Imaging with Deep Learning (MIDL)
12
Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P

Efficient identification of informative features in simulation-based inference

Feb 24, 2022 | Advances in Neural Information Processing Systems, 35, 19260-19273
13
Böhm, J. N., Berens, P., & Kobak, D.

Attraction-repulsion spectrum in neighbor embeddings

Feb 21, 2022 | The Journal of Machine Learning Research, 23(1), 95, 4118–4149
14
Blum, C., Baur, D., Achauer, L. C., Berens, P., [...], Huang, Z., [...] , Macke, J.H., [...] & Ziemann, U.

Personalized neurorehabilitative precision medicine: from data to therapies (MWKNeuroReha)–a multi-centre prospective observational clinical trial to predict long-term outcome of patients with acute motor stroke

Feb 14, 2022 | BMC Neurology, 22(1), 1-15
15
Strauss, S., Korympidou, M. M., Ran, Y., Franke, K., Schubert, T., Baden, T., Berens, P., Euler, T. & Vlasits, A. L

Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina

Feb 07, 2022 | Nature Communications, 13(1), 5574
16
Boreiko, V., Ilanchezian, I., Ayhan, M. S., Müller, S., Koch, L. M., Faber, H., Berens, P. & Hein, M.

Visual explanations for the detection of diabetic retinopathy from retinal fundus images

Feb 02, 2022 | International conference on medical image computing and computer-assisted intervention (MICCAI) (pp. 539-549), Cham: Springer Nature Switzerland