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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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Publication type Article: Conference contribution
Keywords Bias Mitigation ; Cardiac Imaging ; Deep Learning ; Generative Models
Language english
Publication Year 2026
Prepublished in Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 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
Conference Date 23 September 2025
Conference Location Daejeon
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15976 LNCS, Issue: , Pages: 63-73 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30505 - New Technologies for Biomedical Discoveries
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1007/978-3-032-05870-6_7
Scopus ID 105017971902
Erfassungsdatum 2025-10-23
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