Current preprints

[1] C. Karner, J. Gröhl, I. Selby, J. Babar, J. Beckford, T. R Else, T. J Sadler, S. Shahipasand, A. Thavakumar, M. Roberts, J.H.F. Rudd, C.-B. Schönlieb, J. R Weir-McCall, A. Breger*
Parameter choices in HaarPSI for IQA with medical images (under review)
ArXiv: arxiv.org/abs/2410.24098
Related code (PyTorch): https://github.com/ideal-iqa/haarpsi-pytorch (HaarPSI/HaarPSI_MED)

[2] A. Breger*, A. Biguri, M. Sabaté Landman, I. Selby, N. Amberg, E. Brunner, J. Gröhl, S. Hatamikia, C. Karner, L. Ning, S. Dittmer, M. Roberts, AIX-COVNET Collaboration, C.-B. Schönlieb
A study of why we need to reassess full reference image quality assessment with medical images (under revision)
ArXiv: arxiv.org/abs/2405.19097

* Corresponding author

Book

[1] A. Breger*
Basiswissen der mathematischen Bildbearbeitung – Zwischen Theorie und Anwendung
Springer Berlin/Heidelberg, Essentials Series (2024)
https://link.springer.com/book/10.1007/978-3-662-68284-5
DOI: 10.1007/978-3-662-68284-5

Peer-reviewed papers

[1] A. Breger*, C. Karner, I. Selby, J. Gröhl, S. Dittmer, E. Lilley, J. Babar, J.Beckford, T. J Sadler, S. Shahipasand, A. Thavakumar, M. Roberts, C.-B. Schönlieb
A study on the adequacy of common IQA measures for medical images
Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis, Springer Lecture Notes in Electrical Engineering
ArXiv: https://arxiv.org/abs/2405.19224
Related code:
– Evaluation Code and grayscale LIVE annotations: https://github.com/ideal-iqa/iqa-eval
– SpeedyIQA annotation app: https://github.com/selbs/speedy_iqa
– HaarPSI in PyTorch: https://github.com/ideal-iqa/haarpsi-pytorch
– Photoacoustic Data and Annotations: https://zenodo.org/records/13325197

[2] P. Fytas, A. Breger, I. Selby, S. Baker, S. Shahipasand, and A. Korhonen
Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, p.212–235, Bangkok, Thailand, 2024 https://aclanthology.org/2024.bionlp-1.17/

[3] A. Breger*, C. Karner, M. Ehler
visClust: A visual clustering algorithm based on orthogonal projections
Pattern Recognition (Elsevier), vol 148, 2024 download
Code: https://github.com/charmed-univie/visclust

[4] A. Breger*, I. Selby*, M. Roberts, J. Babar, J. Preller, AIX-COVNET Collaboration, J. H.F. Rudd, J. A. D. Aston, J. R. Weir-McCall, and C.-B. Schönlieb
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
Scientific Data (Nature), 2023 Nature Open Access
Online documentation: https://maths.uniofcam.dev/cia/covid-19-projects/nccidxclean/
GitLab Code: https://gitlab.developers.cam.ac.uk/maths/cia/covid-19-projects/nccidxclean

[5] I. Selby, M. Roberts, A. Breger, J. H.F. Rudd, and J. Weir- McCall on behalf of the AIX-COVNET collaboration
Shortcut learning: reduced but not resolved
Radiology, 2023

[6] S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, I. Selby, A. Breger, M. Thorpe, et al.
Navigating the development challenges in creating complex data systems
Nature Machine Intelligence, 2023  

[7] 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
Proceedings MIUA Cambridge, 2022

[8] 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 of SPIE Medical Imaging, 2022

[9] 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

[10] 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.

[11] 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

[12] 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

[13] 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

[14] J. I. Orlando, A. Breger, 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

[15] 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

[16] A. Breger, M. Ehler and M.Gräf
Points on manifolds with asymptotically optimal covering radius arxiv:1607.06899
Journal of Complexity, 2018

[17] 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

[1] 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

[2] 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.

Preliminary work

[1] 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

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