Data & Code
Cultural Heritage Images Analysis & Recovery with extension to Multi-/Hyper-spectral Data (CHARM)
Toolbox: github
Photoacoustic data with IQA annotations: zenodo
CT data with IQA annotations: zenodo
X-ray data with IQA annotations + radiology reports: reshare
Grayscale LIVE data with IQA annotations github
HaarPSI-med in PyTorch: github
Cleaning pipeline NCCID data: gitlab
visClust (clustering algorithm) in Matlab: github
Peer-reviewed published papers
* Corresponding author
[24] A. Breger*. Reconstructing Historical Manuscripts through MSI: The Potential of Contrast in Assessing Image Quality and Legibility. 17th IAPR International Workshop on Document Analysis Systems at 20th International Conference on Document Analysis and Recognition (ICDAR), ArXiv, 2026
[23] S. Hatamikia, A. Breger, C. Karner, B. Pohn, P. MohammadiNasab, M. Buschmann, S. Nougaret, L. Haddad, A. Abbasian Ardakani, A. Mohammadi, P. Apfaltrer, W. Birkfellner, A. Pohl, A. Biguri, G. Kronreif, C.-B. Schönlieb, T. Reynolds. CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking. ArXiv, 2026
[22] A. Breger*, J.Gröhl, C. Karner, T.R. Else, I. Selby, T.Rix, L.-S. Witt, M. Duchêne, J. Weir-McCall, C.-B. Schönlieb. PhotIQA: A photoacoustic image data set with image quality ratings. Scientific Data, Springer Nature Online, 2026
[21] W.Peaslee, A.Breger, C.-B. Schönlieb. Potential Contrast: Properties, Equivalences, and Generalization to Multiple Classes. Proceedings of EUSIPCO conference, ArXiv, 2025
[20] 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. Journal of Imaging Informatics in Medicine, Springer Nature Online, 2025
[19] 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. IEEE Xplore, International Symposium on Biomedical Imaging (ISBI) IEEE Xplore, HaarPSI(MED) in PyTorch, 2025
[18] I. Selby, E. González Solares, A. Breger, M. Roberts, L. Escudero Sánchez, J. H.F. Rudd, N. A. Walton, J. Babar, C.-B. Schönlieb, E. Sala, J. R. Weir-McCall. A pipeline for automated quality control of chest radiographs. Radiology: Artificial Intelligence, 2025
[17] 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 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD), Springer Lecture Notes in Electrical Engineering, arXiv, 2024
[16] 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, Open Access, 2024
[15] A. Breger*, C. Karner, M. Ehler. visClust: A visual clustering algorithm based on orthogonal projections. Pattern Recognition (Elsevier), vol 148, online, 2024
[14] 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), Online documentation, 2023
[13] 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
[12] 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
[11] 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
[10] A.Breger*, F. Goldbach, B.S. Gerendas, U. Schmidt-Erfurth, M. Ehler. Blood vessel segmentation in en-face OCTA images: a frequency based method. Proceedings of SPIE Medical Imaging Conference, 2022 arXiv
[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. NeuroImage, vol. 223, 2021 ScienceDirect
[8] 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. Proceedings in Applied Mathematics and Mechanics, vol. 20, 2021. wiley online
[7] 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. Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), 2020 biorxiv
[6] 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 vol. 10, 2020
[5] 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. Journal of Mathematical Imaging and Vision (JMIV), vol 62, 2020
[4] 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. Springer Lecture Notes in Computer Science (MICCAI), 2019 arXiv
[3] P. Harar, R. Bammer, A. Breger, M. Doerfler, Z. Smekal. Improving Machine Hearing on Limited Data Sets. 11th ICUMT congress (Dublin), 2019 arXiv
[2] A. Breger, M. Ehler and M.Gräf. Points on manifolds with asymptotically optimal covering radius. Journal of Complexity, 2018 arXiv
[1] 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. Eye (Springer Nature), 2017
Peer-reviewed published extended conference abstracts
[6] W. Peaslee, C.-B. Schönlieb, A. Breger. User-guided visualizations of multispectral data for recovering degraded notation. Fantastic Futures (AI4LAM), London, 2025
[5] A. Breger, W. Peaslee. Reconstructing Medieval Music from Multispectral Images: A Case Study. TechnArt Conference, Italy, 2025
[4] W. Peaslee, A. Breger, C.-B. Schönlieb. Multi-Class Normalized Potential Contrast and Applications to Multispectral Images of Manuscripts. TechnArt Conference, Italy, 2025
[3] Ian Selby, A. Breger, Michael Roberts, Lorena Escudero Sánchez, Judith Babar, James Rudd, Evis Sala, Carola-Bibiane Schönlieb, Jonathan Weir-McCall. SpeedyAnnotate: An Intuitive and Open-Source Tool for Efficient Image Annotation and Quality Comparison. RCR Global AI Conference, London, 2025
[2] I. Selby, E. González Solares, A. Breger, M. Roberts, L. Escudero Sánchez, J. Rudd, N. Walton, J. Babar, C.-B. Schönlieb, E. Sala, J. Weir-McCall. Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning. RCR Global AI Conference, London, 2025
[1] A. Breger, Full-Reference Image Quality Assessment for Medical Images, WiMIUA Conference, Cambridge, 2022
Preprints
S. Hatamikia, A. Breger, Clemens Karner, Birgit Pohn, P. MohammadiNasab, M. Buschmann, S. Nougaret, L. Haddad, A. Abbasian Ardakani, A. Mohammadi, P. Apfaltrer, W. Birkfellner, A. Pohl, A. Biguri, G. Kronreif, C.-B. Schönlieb, T. Reynolds. CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking, ArXiv, 2026
P. Fytas, I. Selby, C. Karner, J. Barbar, S. Baker, J. Beckford, T.J. Sadler, S. Shahipasand, A. Thavakumar, J.L. Chen, A. Sawer, M. Roberts, J. Weir-McCall, J.H.F. Rudd, C.-B. Schönlieb, A. Korhonen, A. Breger: Rethinking Clinical Relevance in Chest X-ray Machine
Learning: How Evaluation References Define Performance, ArXiv, 2026
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
Other
[4] 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. 2019 arXiv
[3] A. Breger, M. Ehler, M. Gräf, T. Peter. Cubatures on Grassmannians: moments, dimension reduction, and related topics. Compressed Sensing and its Applications: MATHEON Workshop 2015 (Applied and Numerical Harmonic Analysis), 2017 arXiv
[2] A. Breger, M. Ehler and M.Gräf. Quasi Monte Carlo integration and kernel-based function approximation on Grassmannians. Frames and Other Bases in Abstract and Function Spaces, Applied and Numerical Harmonic Analysis series (ANHA, Birkhauser/Springer), 2017 arXiv
[1] A. Breger*. On image segmentation and applications in clinical retinal analysis.
Master’s thesis, University of Vienna, 2015. E-Thesis