Publications

Peer-reviewed

  • I. Selby, E. G. Solares, A. Breger, M. Roberts, L. Escudero, J. H.F. Rudd, J. Babar, N. A. Walton, E. Sala, and C.-B. Schönlieb
    Automated Quality Control of Chest X-Rays
    Submitted (2022)
  • A.Breger, F. Goldbach, B.S. Gerendas, U. Schmidt-Erfurth, M. Ehler
    Blood vessel segmentation in en-face OCTA images: a frequency based method, arXiv:2109.06116
    Proceedings SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis
  • F. Zhang, A.Breger, K.Cho, L.Ning, C.-F. Westin, L. J. O’Donnell, and O. Pasternak
    Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI ScienceDirect
    NeuroImage, vol. 223, 2021
  • A.Breger, G. Ramos Llorden, G. Vegas Sanchez – Ferrero, W. S. Hoge, M. Ehler, C.-F. Westin
    Orthogonal projections for image quality analyses applied to MRI wiley online
    Proceedings in Applied Mathematics and Mechanics, vol. 20, 2021.
  • F. Zhang, A.Breger, K.Cho, L.Ning, C.-F. Westin, L. J. O’Donnell, and O. Pasternak
    Deep Learning Based Brain Tissue Segmentation of Diffusion MRI from Novel Diffusion Kurtosis Imaging Features biorxiv
    Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), 2020
  • J.I. Orlando, B.S. Gerendas, S. Klimscha, C. Grechenig, A. Breger, M. Ehler, S.M. Waldstein, H. Bogunovic, U. Schmidt-Erfurth
    Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning Nature Scientific Reports
    Scientific Reports vol. 10, 2020
  • A. Breger, J. I. Orlando, P. Harar, M. Doerfler, S. Klimscha, C. Grechenig, B. S. Gerendas, U. Schmidt-Erfurth, and M. Ehler
    On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems Springer JMIV
    Journal of Mathematical Imaging and Vision (JMIV), vol 62, 2020
  • A. Breger, J. I. Orlando, H. Bogunović, S. Riedl, B. S. Gerendas, M. Ehler, U. Schmidt-Erfurth
    An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans, arXiv:1908.00764
    Springer Lecture Notes in Computer Science (MICCAI), 2019
  • P. Harar, R. Bammer, A. Breger, M. Doerfler, Z. Smekal
    Improving Machine Hearing on Limited Data Sets
    11th ICUMT congress (Dublin), 2019 arxiv:1903.08950
  • A. Breger, M. Ehler and M.Gräf
    Points on manifolds with asymptotically optimal covering radius, arxiv:1607.06899
    Journal of Complexity, 2018
  • A. Breger, M. Ehler, H. Bogunovic, S.M. Waldstein, A. Philip, U. Schmidt-Erfurth, B.S. Gerendas
    Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images, Epub
    Eye (Springer Nature), 2017

Book Chapters

  • A. Breger, M. Ehler, M. Gräf, T. Peter
    Cubatures on Grassmannians: moments, dimension reduction, and related topics, arXiv:1705.02978
    Compressed Sensing and its Applications: MATHEON Workshop 2015 (Applied and Numerical Harmonic Analysis), 2017
  • A. Breger, M. Ehler and M.Gräf
    Quasi Monte Carlo integration and kernel-based function approximation on Grassmannians, arXiv:1605.09165
    Frames and Other Bases in Abstract and Function Spaces, Applied and Numerical Harmonic Analysis series (ANHA, Birkhauser/Springer), 2017.

Preprints

  • A.Breger, G. Ramos Llorden, G. Vegas Sanchez – Ferrero, W. S. Hoge, M. Ehler, C.-F. Westin
    On the reconstruction accuracy of multi-coil MRI with orthogonal projections, arXiv:1910.13422 (2019)

Other

  • A. Breger
    On image segmentation and applications in clinical retinal analysis.
    Master’s thesis, University of Vienna, 2015. E-Thesis