Peer-reviewed

• A. Breger, C. Karner, M. Ehler
  visClust: A visual clustering algorithm based on orthogonal projections
  Submitted (2022), preprint: arXiv
  
• 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