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Skorupko, G.* ; Osuala, R. ; Szafranowska, Z.* ; Kushibar, K.* ; Dang, V.N.* ; Aung, N.* ; Petersen, S.E.* ; Lekadir, K.* ; Gkontra, P.*

Fairness-Aware Data Augmentation for Cardiac MRI Using Text-Conditioned Diffusion Models.

In: (3rd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2025, held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 63-73 (Lect. Notes Comput. Sc. ; 15976 LNCS)
DOI
While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased training datasets. To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes such as sex, age, body mass index (BMI), and health condition. We adopt ControlNet based on a denoising diffusion probabilistic model to condition on text assembled from patient metadata and cardiac geometry derived from segmentation masks. We assess our method using a large-cohort study from the UK Biobank by evaluating the realism of the generated images using established quantitative metrics. Furthermore, we conduct a downstream classification task aimed at debiasing a classifier by rectifying imbalances within underrepresented groups through synthetically generated samples. Our experiments demonstrate the effectiveness of the proposed approach in mitigating dataset imbalances, such as the scarcity of diagnosed female patients or individuals with normal BMI level suffering from heart failure. This work represents a major step towards the adoption of synthetic data for the development of fair and generalizable models for medical classification tasks. Notably, we conduct all our experiments using a single, consumer-level GPU to highlight the feasibility of our approach within resource-constrained environments. Our code is available at https://github.com/faildeny/debiasing-cardiac-mri.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Bias Mitigation ; Cardiac Imaging ; Deep Learning ; Generative Models
Sprache englisch
Veröffentlichungsjahr 2026
Prepublished im Jahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 3rd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2025, held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Konferzenzdatum 23 September 2025
Konferenzort Daejeon
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15976 LNCS, Heft: , Seiten: 63-73 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
DOI 10.1007/978-3-032-05870-6_7
Scopus ID 105017971902
Erfassungsdatum 2025-10-23
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