Philipp Seeböck

Contact

Philipp Seeböck

Doctoral Research Scientist
CIR Computational Imaging Research Lab
Department of Biomedical Imaging and Image-guided Therapy
Medical University of Vienna

Email: philipp.seeboeck (at) meduniwien.ac.at

Phone: +43 1 40400 73564

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

Research interests

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

Current Projects

Optima500px Logo

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.

 

Seleted recent 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., 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. [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]

- Philipp Seeböck. Deep Learning In Medical Image Analysis. Master’s thesis, Technical University of Vienna, Austria, 2015.