In the Department of Data Science we generate knowledge from data to advance neuroscience and ophthalmology.
01
Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.
Sparse visual counterfactual explanations in image space
02
Müller, S., Koch, L. M., Lensch, H., & Berens, P.
A Generative Model Reveals the Influence of Patient Attributes on Fundus Images
03
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.
Hidden in plain sight: Subgroup shifts escape ood detection
04
Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P
Efficient identification of informative features in simulation-based inference
05
06
Böhm, J. N., Berens, P., & Kobak, D.
Attraction-repulsion spectrum in neighbor embeddings
07
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
08
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
09
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
10
Oesterle, J., Krämer, N., Hennig, P., & Berens, P.
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models
11
Faber, H., Berens, P., & Rohrbach, J. M.
Ocular changes as a diagnostic tool for malaria
12
Ayhan, M. S., Kümmerle, L. B., Kühlewein, L., Inhoffen, W., Aliyeva, G., Ziemssen, F., & Berens, P.
Clinical validation of saliency maps for understanding deep neural networks in ophthalmology
13
Behrens, C., Yadav, S. C., Korympidou, M. M., Zhang, Y., Haverkamp, S., Berens, P. & Schubert, T.