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Cambridge Image Analysis

Dr Anna Breger

Anna Breger is a senior postdoctoral researcher in the Cambridge Image Analysis Group at the DAMTP, University of Cambridge (UK) and a member of the global COVID-19 AIX-COVNET collaboration where she is focusing on research with X-Ray data. Moreover, she is holding the prestigious Hertha Firnberg fellowship funded by the Austrian Science Fund. With that she is leading the research project iDeal based at the Medical University of Vienna, focusing on image data visualisation and evaluation.

Her current main interests are applications of mathematical image processing in medical problems and beyond, including research on data representations, dimension reduction and image quality assessment.

 

Publications

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
– J Imaging Inform Med
(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, members of the AIX-COVNET collaboration
– Radiol Artif Intell
(2025)
7,
e240003
(doi: 10.1148/ryai.240003)
PARAMETER CHOICES IN HAARPSI FOR IQA WITH MEDICAL IMAGES
A Breger, M Roberts, J Rudd, C Schoenlieb, J Weir-McCall
– Arxiv
(2024)
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)
(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)
Shortcut Learning: Reduced But Not Resolved.
IA Selby, M Roberts, A Breger, JHF Rudd, JR Weir-McCall
– Radiology
(2023)
308,
e230379
(doi: 10.1148/radiol.230379)
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
A Breger, I Selby, M Roberts, J Babar, E Gkrania-Klotsas, J Preller, L Escudero Sanchez, J Rudd, J Aston, J Weir-McCall, E Sala, C Schoenlieb
– Scientific Data
(2023)
10,
493
(doi: 10.1038/s41597-023-02340-7)
Navigating the development challenges in creating complex data systems
S Dittmer, M Roberts, J Gilbey, A Biguri, I Selby, A Breger, M Thorpe, JR Weir-McCall, E Gkrania-Klotsas, A Korhonen, E Jefferson, G Langs, G Yang, H Prosch, J Stanczuk, J Tang, J Babar, L Escudero Sánchez, P Teare, M Patel, M Wassin, M Holzer, N Walton, P Lió, T Shadbahr, E Sala, J Preller, JHF Rudd, JAD Aston, CB Schönlieb
– Nature Machine Intelligence
(2023)
5,
681
(doi: 10.1038/s42256-023-00665-x)
Deep learning based segmentation of brain tissue from diffusion MRI
F Zhang, A Breger, KIK Cho, L Ning, C-F Westin, LJ O'Donnell, O Pasternak
– Neuroimage
(2021)
233,
117934
(doi: 10.1016/j.neuroimage.2021.117934)
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Research Group

Cambridge Image Analysis

Email

ab2864@cam.ac.uk

Room

F1.04

Study at Cambridge

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Research at Cambridge