Understanding the eye through data — from single synapses to population-scale disease.
01
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
02
Yaïci, R., Cieplucha, M., Bock, R. et al
ChatGPT und die deutsche Facharztprüfung für Augenheilkunde: eine Evaluierung
03
Koch, L.M., Baumgartner, C.F. & Berens, P
Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study
04
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.
05
González-Márquez, R., Schmidt, L., Schmidt, B. M., Berens, P., & Kobak, D.
The landscape of biomedical research
06
Grote, T., Berens, P.
A paradigm shift?—On the ethics of medical large language models
07
Sun, S., Koch, L. M., & Baumgartner, C. F.
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
08
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
09
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
10
Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner,, C.F.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
11
Grote, T., & Berens, P
Uncertainty, evidence, and the integration of machine learning into medical practice
12
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
13
Böhm, J. N., Berens, P., & Kobak, D.
Unsupervised visualization of image datasets using contrastive learning
14
Damrich, S., Böhm, J. N., Hamprecht, F. A., & Kobak, D.
From t-SNE to UMAP with contrastive learning
15
Bachmann, F., Hennig, P., Kobak, D.
Wasserstein tSNE
16
Janschewski, J., Käppler, C., & Berens, P.