Computational Imaging Research

Philipp Seeböck, Dipl.-Ing., PhD

Postdoctoral Researcher
Department of Ophthalmology and Optometry
Medical University of Vienna
Email: philipp.seeboeck(at)
Phone: +43 1 40400 73564
Office: Anna Spiegel building (Building 25.2, room 25.07.036)


Philipp Seeböck is currently a postdoctoral research scientist at the Medical University of Vienna and the Director of the IT Systems of the Vienna Reading Center. He studied Medical Informatics at Vienna University of Technology and finished his Master in 2015. He finished his PhD as Doctoral Research Scientist of the OPTIMA lab and the Computational Image Analysis and Radiology Lab (CIR) at the Medical University of Vienna in July 2019. His current research interests are the development of machine/deep learning techniques for ophthalmic and medical image analysis.

Research Interests:

– Deep Learning
– Unsupervised Learning in Medicine
– Medical Image Analysis

Selected Publications:
Seeböck P, Waldstein SM, Donner R, Sadeghipour A, Bogunovic H, Osborne A, Schmidt-Erfurth U. “Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning”. Scientific Reports. 2020. [pdf]

Seeböck P, Romo-Bucheli D, Orlando JI, Gerendas BS, Waldstein SM, Schmidt-Erfurth U, Bogunovic H. “Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina”. Biomedical Optics Express. 2020. [pdf]

Seeböck, P., Orlando, J.I., Schlegl, T., Waldstein, S., Bogunovic, H., Klimscha, S., Langs, G., Schmidt-Erfurth, U. “Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT”. IEEE Transactions on Medical Imaging. 2019. [pdf] [supplementaryMaterial]

Seeböck, P., Vogl, W., Waldstein, S.M., Baratsits, M., Orlando, J.I., Alten, T., Bogunovic, H., Arikan, M., Mylonas, G., Schmidt-Erfurth, U. “Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning”. ARVO 2019. (MIT Outstanding Poster Award) [pdf]

Seeböck, P., Romo-Bucheli, D. , Waldstein, S. , Bogunovi?, H., Orlando, J.I., Gerendas, B.S., Langs, G., Schmidt-Erfurth, U. “Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation.”  IEEE International Symposium on Biomedical Imaging (ISBI) 2019. [pdf]

Seeböck, P., Waldstein, S., Klimscha, S., Bogunovic, H., Schlegl, T., Gerendas, B. S.,  Donner, R., Schmidt-Erfurth, U., Langs, G. “Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data”. IEEE Transactions on Medical Imaging. 2019. [pdf] [supplementaryMaterial]

Seeböck, P., Donner, R., Schlegl, T., & Langs, G. “Unsupervised Learning for Image Category Detection”. Proceedings of the 22nd Computer Vision Winter Workshop. 2017. (Best Paper Award) [pdf]

Seeböck, P., Waldstein, S., Klimscha, S., Gerendas, B. S., Donner, R., Schlegl, T.,  Langs, G. “Identifying and Categorizing Anomalies in Retinal Imaging Data”. NIPS Workshop on Machine Learning for Health. 2016. [pdf]

Seeböck P. “Discovery of Biomarker Candidates in Retinal OCT Images using Deep Learning“. Doctoral Thesis . 2019, Vienna, Medical University of Vienna