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
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
03
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)
04
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
05
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
06
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)
07
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)
08
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection

May 16, 2023 | In Medical Imaging with Deep Learning.
09
Müller, S., Koch, L. M., Lensch, H., & Berens, P.

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

May 09, 2023 | In Medical Imaging with Deep Learning.
10
Donteu, K. R. D., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., ... & Koch, L. M.

Sparse Activations for Interpretable Disease Grading

Apr 05, 2023 | In Medical Imaging with Deep Learning.
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
12
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)
13
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.

Hidden in plain sight: Subgroup shifts escape ood detection

Jul 01, 2022 | Proceedings of Medical Imaging with Deep Learning (MIDL)
15
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
16
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