  The progress in deep learning solutions for disease diagnosis and prognosis
based on cardiac magnetic resonance imaging is hindered by highly imbalanced
and biased training data. 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, 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 using a large-cohort study,
specifically, the UK Biobank. We assess our method 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
younger 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.
