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Thomas Schlegl
Dipl.-Ing. Thomas Schlegl, PhD BSc

Center for Medical Physics and Biomedical Engineering
Position: Research Associate (Postdoc)

ORCID: 0000-0003-0706-7876
T +43 1 40400 39223


Artificial Intelligence; Diagnostic Imaging; Image Enhancement; Imaging, Three-Dimensional; Medical Informatics; Medical Physics; Multimodal Imaging; Neural Networks (Computer); Ophthalmology; Pattern Recognition, Automated; Radiology; Tomography, Optical Coherence

Research group(s)

Research interests

My main research focus is the development of algorithms to process, analyze, generate and grade (bio-)medical imaging data of various imaging modalities.

  • Medical image analysis
  • Optical Coherence Tomography (OCT)
  • Anomaly Detection (with Generative Adversarial Networks: AnoGAN, f-AnoGAN)
  • Application of Computer Vision and Machine Learning methods on biomedical data
  • Multimodal Learning of deep representations based on visual and textual (clinical) data

Techniques, methods & infrastructure

Machine Learning with main focus on (Deep) Neural Networks (Deep Learning)

Selected publications

  1. Nienhaus, J., Matten, P., Britten, A., Scherer, J., Höck, E., Freytag, A., Drexler, W., Leitgeb, R.A., Schlegl, T., Schmoll T. (2023) ‘Live 4D-OCT denoising with self-supervised deep learning’, Scientific Reports, 13(1). Available at:
  2. Niederleithner, M., De Sisternes, L., Stino, H., Sedova, A., Schlegl, T., Bagherinia, H., Britten, A., Matten, P., Schmidt-Erfurth, U., Pollreisz, A., Drexler, W., Leitgeb, R.A., Schmoll, T. (2023) "Ultra-Widefield OCT Angiography," in IEEE Transactions on Medical Imaging, vol. 42, no. 4, pp. 1009-1020, doi: 10.1109/TMI.2022.3222638.
  3. Schlegl, T., Stino, H. , Niederleithner, M., Pollreisz, A., Schmidt-Erfurth, U., Drexler, W., Leitgeb, R.A., Schmoll, T. (2022) "Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images." arXiv preprint arXiv:2208.03868
  4. Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U. (2019). "f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks. Medical Image Analysis, 54, 30-44."
  5. Schlegl, T., Waldstein, S.M., Bogunovic, H., Endstraßer, F., Sadeghipour, A., Philip, A.-M., Podkowinski, D., Gerendas, B. S., Langs, G., Schmidt-Erfurth, U. (2018). "Fully automated detection and quantification of macular fluid in OCT using deep learning." Ophthalmology, 125(4), 549-558.