Dipl. Ing. Philipp Seeböck, PhD
Department of Biomedical Imaging and Image-guided Therapy
Position: Research Associate (Postdoc)
T +43 1 40400 - 73922
philipp.seeboeck@meduniwien.ac.at
Keywords
Artificial Intelligence; Biomedical Research; Medical Informatics
Research group(s)
- Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA)
Research Area: Since 2013 the interdisciplinary OPTIMA group is pioneering in the introduction of artificial intelligence into ophthalmology.
Members: - Computational Imaging Research Lab
Head: Georg Langs
Research Area: Research at CIR is roughly grouped around 3 research lines: Machine Learning & Neuroimaging, Computer Aided Diagnosis and Quantification, Computer Vision and Pattern Recognition
Members: - Vienna Reading Center
Research Area: Our mission is to enhance progress in the scientific understanding of diseases and treatment through efficient and reliable analysis of ophthalmic images in clinical studies.
Members:
Research interests
- Machine Learning
- Deep Learning
- Medical Image Analysis
- Biomarker Detection
- Representation Learning
- Anomaly Detection
Selected publications
- Seeböck, P. et al. (2022) ‘Linking Function and Structure with ReSensNet’, Ophthalmology Retina, 6(6), pp. 501–511. Available at: http://dx.doi.org/10.1016/j.oret.2022.01.021.
- Seeböck, Philipp, et al. "Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT." IEEE transactions on medical imaging 39.1 (2019): 87-98.
- Seeböck, Philipp, et al. "Unsupervised identification of disease marker candidates in retinal OCT imaging data." IEEE transactions on medical imaging 38.4 (2018): 1037-1047.
- Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. (2019). f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis.
- Hofer, D., Schmidt-Erfurth, U., Orlando, J. I., Goldbach, F., Gerendas, B. S., & Seeböck, P. (2022). Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures. Biomedical Optics Express, 13(5), 2566-2580.