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

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Zouridis, I. S., Schmors, L., Fischer, K. M., Berens, P., Preston-Ferrer, P., & Burgalossi, A.

Juxtacellular recordings from identified neurons in the mouse locus coeruleus

Jun 13, 2024 | European Journal of Neuroscience
02
Köhler, P., Fadugba, J., Berens, P., Koch, L.M.

Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images

Jun 06, 2024 | Medical Imaging with Deep Learning 2024
03
Wundram, A.M., Fischer, P., Wunderlich, S., Faber, H., Koch, L.M., Berens, P., Baumgartner C. F.

Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach

Jun 06, 2024 | Medical Imaging with Deep Learning 2024
04
Yaïci, R., Cieplucha, M., Bock, R. et al

ChatGPT und die deutsche Facharztprüfung für Augenheilkunde: eine Evaluierung

May 27, 2024 | Die Ophthalmologie
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Ayhan, M. S., Neubauer, J., Uzel, M. M., Gelisken, F., & Berens, P.

Interpretable detection of epiretinal membrane from optical coherence tomography with deep neural networks.

Apr 11, 2024 | Scientific Reports, 14(1), 8484.
07
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.

The landscape of biomedical research

Apr 09, 2024 | Patterns, 100968
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Grote, T., Berens, P.

A paradigm shift?—On the ethics of medical large language models

Mar 25, 2024 | Bioethics
09
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.
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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
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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)
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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
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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
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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
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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)
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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)