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Cambridge Image Analysis

Dr Anna Breger

Anna Breger is an Assistant Research Professor in the Cambridge Image Analysis Group at the DAMTP, University of Cambridge (UK) and a member of the AI for cultural heritage hub (ArCH) Cambridge. She is leading the research project Non-invasive imaging and machine learning techniques for the reconstruction of degraded historical sheet music in collaboration with the cultural heritage imaging laboratory (CHIL) at the University Library, the Fitzwilliam museum and the Gonville & Caius library in Cambridge, aiming to reconstruct/transcribe lost historical music notation in degraded manuscripts. 

Previously, she had been a member of the global AIX-COVNET collaboration working with medical images obtained during the covid-19 pandemic and from 2022-2025 she held the prestigious Hertha Firnberg fellowship funded by the Austrian Science Fund, leading a research project on image quality asssessment for applications with medical images.

 

Publications

Potential Contrast: Properties, Equivalences, and Generalization to Multiple Classes
P Wallace, A Breger, C-B Schönlieb
(2025)
PhotIQA: A photoacoustic image data set with image quality ratings
A Breger, J Gröhl, C Karner, TR Else, I Selby, J Weir-McCall, C-B Schönlieb
(2025)
(doi: 10.48550/arxiv.2507.03478)
A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images.
A Breger, A Biguri, MS 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
– Journal of Imaging Informatics in Medicine
(2025)
1
(doi: 10.1007/s10278-025-01462-1)
A Pipeline for Automated Quality Control of Chest Radiographs.
IA Selby, E González Solares, A Breger, M Roberts, L Escudero Sánchez, J Babar, JHF Rudd, NA Walton, E Sala, C-B Schönlieb, JR Weir-McCall, AIX-COVNET Collaboration
– Radiology. Artificial intelligence
(2025)
7,
e240003
(doi: 10.1148/ryai.240003)
SpeedyAnnotate: An Intuitive and Open-Source Tool for Efficient Image Annotation and Quality Comparison
I Selby, A Breger, M Roberts, LE Sánchez, J Babar, J Rudd, E Sala, C-B Schönlieb, J Weir-McCall
– The Royal College of Radiologists Open
(2025)
3,
100233
(doi: 10.1016/j.rcro.2024.100233)
Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning
I Selby, EG Solares, A Breger, M Roberts, LE Sánchez, J Rudd, N Walton, J Babar, C-B Schönlieb, E Sala, J Weir-McCall
– The Royal College of Radiologists Open
(2025)
3,
100232
(doi: 10.1016/j.rcro.2024.100232)
Parameter Choices in Haarpsi for IQA with Medical Images
C Karner, J Gröhl, I Selby, J Babar, J Beckford, TR Else, TJ Sadler, S Shahipasand, A Thavakumar, M Roberts, JHF Rudd, C-B Schönlieb, JR Weir-McCall, A Breger
– Arxiv
(2024)
00,
1
(doi: 10.1109/isbi60581.2025.10981227)
A study on the adequacy of common IQA measures for medical images
A Breger, C Karner, I Selby, J Gröhl, S Dittmer, E Lilley, J Babar, J Beckford, TR Else, TJ Sadler, S Shahipasand, A Thavakumar, M Roberts, C-B Schönlieb
– Springer Lecture Notes in Electrical Engineering, MICAD conference (2024)
(2024)
(doi: 10.48550/arxiv.2405.19224)
(link to publication)
visClust: A visual clustering algorithm based on orthogonal projections
A Breger, C Karner, M Ehler
– Pattern Recognition
(2024)
148,
110136
(doi: 10.1016/j.patcog.2023.110136)
Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology.
P Fytas, A Breger, I Selby, S Baker, S Shahipasand, A Korhonen
– Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
(2024)
212
(doi: 10.18653/v1/2024.bionlp-1.17)
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Research Group

Cambridge Image Analysis

Email

ab2864@cam.ac.uk

Room

F2.05

Study at Cambridge

About the University

Research at Cambridge