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Seeböck Philipp

  1. Universitätsklinik für Radiologie und Nuklearmedizin
  2. Unsere Abteilungen
  3. CIR
  4. Team
  5. Seeböck Philipp
Philipp Seeböck|Dr. Sebastian Röhrich|© Ines Fötschl

Philipp Seeböck

Senior Researcher and Post Doc (Faculty)

 - Senior Researcher and Post Doc (Faculty)
 - Head of Medical Anomaly Detection (MANO) Group
 - Head of CIR Data Engineering and Management

Contact

Email: philipp.seeboeck@meduniwien.ac.at
Phone: +43 1 40400 73922

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
  • Medical Image Analysis
  • Anomaly Detection
  • Representation Learing (Unsupervised/Self-Supervised/...)

Short CV

Philipp Seeböck completed his Computer Science studies at Vienna University of Technology with a specialization in Medical Informatics in 2015. After his studies, he did his PhD in the OPTIMA research group, working in the interdisciplinary fileds of retinal imaging, Medical Image Analysis and Artificial Intelligence. After completing his doctorate, he was head of IT systems at the Vienna Reading Center (VRC) from 2019 to 2022, simultaneously working as a postdoc at Department of Ophthalmology at the Medical University of Vienna. He is is currently a postdoctoral research scientist in the CIR lab at the Medical University of Vienna.
He is also a member of the European Radiology Scientific Editorial Board and has a strong record in deep learning and medical image analysis, particularly in anomaly detection to disentangle healthy from abnormal variability, domain adaptation, representation learning and patient outcome prediction. Besides research, he has also been chairman of a local football club in Vienna for more than 10 years.
 

  • Seeböck, P., Orlando, J. I., Michl, M., Mai, J., Erfurth, U. S., & Bogunović, H. "Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection". Medical Image Analysis. 2024. [pdf]

  • Burger, B., Bernathova, M., Seeböck, P., Singer, C. F., Helbich, T. H., & Langs, G. "Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study". European Radiology Experimental. 2023. [pdf]

  • Reiter, G. S., Bogunovic, H., Schlanitz, F., Vogl, W. D., Seeböck, P., Ramazanova, D., & Schmidt-Erfurth, U. "Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degeneration". Eye. 2023. [pdf]

  • Kocak, B., Chepelev, L. L., Chu, L. C., Cuocolo, R., Kelly, B. S., Seeböck, P., ... & Pinto dos Santos, D. "Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology". European Radiology. 2023. [pdf]

  • König, M.*, Seeböck, P.*, Gerendas, B. S., Mylonas, G., Winklhofer, R., Dimakopoulou, I., & Schmidt-Erfurth, U. M. Quality assessment of colour fundus and fluorescein angiography images using deep learning. British Journal of Ophthalmology. 2022. [pdf] *Contributed equally.

  • Schwarzenbacher L.*, Seeböck P.*, Schartmüller D., Leydolt C., Menapace R., Schmidt-Erfurth U. „Artificial Intelligence in Anterior Segment OCT Imaging: Automatic Segmentation of IOL, the Retrolental Space and Berger’s Space using a Deep Learning Algorithm”. Acta Ophthalmologica. 2022. [pdf] *Contributed equally.

  • Holomcik D.*, Seeböck P.*, Gerendas B.S., Mylonas G., Najeeb B.H.; Schmidt-Erfurth U., Deak G. “Segmentation of Macular Neovascularization and Leakage in Fluorescein Angiography Images in Neovascular Age-Related Macular Degeneration using Deep Learning”. Nature Eye. 2022. [pdf] *Contributed equally.

  • Hofer D., Schmidt-Erfurth U., Orlando J.I., Goldbach F., Gerendas B.S., Seeböck P. "Improving Foveal Avascular Zone Segmentation in Fluorescein Angiograms by Leveraging Manual Vessel Labels from Public Color Fundus Pictures". Biomedical Optics Express. 2022. [pdf]

  • Seeböck P., Vogl W.D., Waldstein S.M., Orlando J.I., Baratsits M., Alten T., Arikan M., Mylonas G., Bogunović B., Schmidt-Erfurth U. “Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning”. Ophthalmology Retina. 2022. [pdf]

  • Waldstein S.M.*, Seeböck P.*, 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] *Contributed equally.

  • 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]
  • Seeböck P. “Discovery of Biomarker Candidates in Retinal OCT Images using Deep Learning“. Doctoral Thesis. 2019, Vienna, Medical University of Vienna. [pdf]
  • Philipp Seeböck. Deep Learning In Medical Image Analysis. Master’s thesis, Technical University of Vienna, Austria, 2015.