Philipp Seeböck, Dipl.-Ing., PhD


Postdoctoral Researcher

Christian Doppler Laboratory for Ophthalmic Image Analysis

Department of Ophthalmology

Medical University of Vienna

Email: philipp.seeboeck(at)

Phone: +43 1 40400 73564

Office: Rektoratsgebäude (Building 88, floor 2, room 206)


Philipp Seeböck is currently a postdoctoral research scientist at the Medical University of Vienna and the Head 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. "Deep Learning In Medical Image Analysis". Master’s thesis, Technical University of Vienna, Austria, 2015. [pdf]