Key publications

1. Gorgi Zadeh S., Wintergerst M.W., Wiens V., Thiele S., Holz F.G., Finger R.P., Schultz T.: CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography.  In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 65–73. Springer, 2017.   URL  http://dx.doi.org/10.1007/978-3-319-67558-9_8
2. Wintergerst M.W.^*, Gorgi Zadeh S.^*, Wiens V., Thiele S., Schmitz-Valckenberg S., Holz F.G., Finger R.P., Schultz T.: Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography.  In: Scientific Reports, vol. 10(1), pp. 1–7, 2020.   URL  http://dx.doi.org/10.1038/s41598-020-63924-6 . * Contributed equally            
3. Gorgi Zadeh S., Wintergerst M.W., Schultz T.: Intelligent interaction and uncertainty visualization for efficient drusen and retinal layer segmentation in Optical Coherence Tomography.  In: Computers & Graphics, vol. 83, pp. 51–61, 2019.   URL  http://dx.doi.org/10.1016/j.cag.2019.07.001
4. Gorgi Zadeh S., Wintergerst M.W., Schultz T.: Uncertainty-Guided Semi-Automated Editing of CNN-based Retinal Layer Segmentations in Optical Coherence Tomography.  In: Proc. Visual Computing for Biology and Medicine, pp. 107–115. 2018.   URL  http://dx.doi.org/10.2312/vcbm.20181235
5. Gorgi Zadeh S., Didas S., Wintergerst M.W., Schultz T.: Multi-scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization.  In: Journal of Mathematical Imaging and Vision, vol. 59(2), pp. 257–269, 2017.   URL  http://dx.doi.org/10.1007/s10851-017-0729-1
6. Wu Z., Luu C.D., Hodgson L.A., Caruso E., Brassington K.H., Tindill N., Aung K.Z., Harper C.A., Wickremasinghe S.S., Sandhu S.S., McGuinness M. B., Chen F. K., Chakravarthy U., Arnold J. J., Heriot W. J., Durkin S. R., Wintergerst M. WM., Gorgi Zadeh S., et al.: Secondary and Exploratory Outcomes of the Subthreshold Nanosecond Laser Intervention Randomized Trial in Age-Related Macular Degeneration: A LEAD Study Report.  In: Ophthalmology Retina, vol. 3(12), pp. 1026–1034, 2019.   URL  http://dx.doi.org/10.1016/j.oret.2019.07.008
7. Gorgi Zadeh, S., & Schmid, M..: Bias in cross-entropy-based training of deep survival networks. IEEE transactions on pattern analysis and machine intelligence, (2020). URL http://dx.doi.org/10.1109/TPAMI.2020.2979450
8. Gorgi Zadeh, S., Behning C., and Schmid M.. “An imputation approach using subdistribution weights for deep survival analysis with competing events.” Scientific Reports 12.1 (2022): 3815. URL https://doi.org/10.1038/s41598-022-07828-7
9. Gorgi Zadeh, S., Hermann, M.,  Merklinger, E., Schloetel, J., and Schultz, T.. “Quantifying image structures in high-throughput microscopy with total variation flow.” 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, 2016. URL https://doi.org/10.1109/ISBI.2016.7493288 .
10. Gutnikov, A., Hähn-Schumacher, P., Ameln, J, Gorgi Zadeh, S., Schultz, T., Harmening, W.. et al. “Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography”.  Scientific Reports15 (2025): 23858. https://doi.org/10.1038/s41598-025-08028-9 .