Data Science / Machine Learning for Safe Medical Diagnostics
We work on robust and interpretable algorithms that enable machine learning algorithms to be applied in the clinical workflow in ophthalmology.
01
Grote, T., Freiesleben, T. & Berens, P.
Foundation models in healthcare require rethinking reliability
02
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
03
Köhler, P., Fadugba, J., Berens, P., Koch, L.M.
Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images
04
Koch, L.M., Baumgartner, C.F. & Berens, P
Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study
05
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.
06
Sun, S., Koch, L. M., & Baumgartner, C. F.
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
07
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
08
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
09
Sun, S., Woerner, S., Maier, A., Koch, L.M., Baumgartner,, C.F.
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
10
Boreiko, V., Augustin, M., Croce, F., Berens, P., & Hein, M.
Sparse visual counterfactual explanations in image space
11
Müller, S., Koch, L. M., Lensch, H., & Berens, P.
A Generative Model Reveals the Influence of Patient Attributes on Fundus Images
12
Koch, L. M., Schürch, C. M., Gretton, A., & Berens, P.
Hidden in plain sight: Subgroup shifts escape ood detection
13
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
14
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
15
Faber, H., Berens, P., & Rohrbach, J. M.
Ocular changes as a diagnostic tool for malaria
16
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
17
Pfau, M., Walther, G., von der Emde, L., Berens, P., Faes, L., Fleckenstein, M., ... & Holz, F. G.
Artificial intelligence in ophthalmology: Guidelines for physicians for the critical evaluation of studies.
18
Ayhan, M. S., Kühlewein, L., Aliyeva, G., Inhoffen, W., Ziemssen, F., & Berens, P.
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.
19
Berens, P., Waldstein, S. M., Ayhan, M. S., Kuemmerle, L., Agostini, H., Stahl, A., & Ziemssen, F.
Potential of methods of artificial intelligence for quality assurance.
20
Grote, T., & Berens, P.
On the ethics of algorithmic decision-making in healthcare.
21
Berens, P., & Ayhan, M. S.