Philipp Seeböck
Doctoral Research ScientistContact
Email: philipp.seeboeck@meduniwien.ac.at
Phone: +43 1 40400 73722
Computational Imaging Research Lab
Department of Biomedical Imaging and Image-guided Therapy
Medical University of Vienna
Waehringer Guertel 18-20
A-1090 Vienna / Austria
Office:
Anna Spiegel Center of Translational Research
(Building 25, floor 7, room 27)
Research interests
- Deep Learning
- Unsupervised Learning in Medicine
- Medical Image Analysis
Current Projects
OPTIMA - Christian Doppler Laboratory on Ophtalmic Image Analysis. funded by the Christian Doppler Gesellschaft. The OPTIMA project aims at individualizing patient management and at lowering treatment and monitoring needs in ophtalmic diseases to make the most effective ocular treatment available to all patients and physicians. We are conducting methodological research on big spatio-temporal data analysis.
- 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
- 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.
- Seeböck, P., Romo-Bucheli, D. , Waldstein, S. , Bogunovic, 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.
- 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]
- Philipp Seeböck. Deep Learning In Medical Image Analysis. Master’s thesis, Technical University of Vienna, Austria, 2015.