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

TL;DR

This paper introduces a method using text-conditioned diffusion models to generate synthetic cardiac MRI data, aiming to balance datasets and improve fairness in disease classification, especially for underrepresented groups.

Contribution

The work presents a novel application of ControlNet diffusion models conditioned on patient metadata to generate realistic synthetic cardiac MRI images for dataset balancing.

Findings

Synthetic data effectively reduces class imbalances.

Improved classifier fairness on underrepresented groups.

Method is feasible with consumer-level GPU hardware.

Abstract

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.