  Chest X-rays (CXR) are the most common medical imaging study and are used to
diagnose multiple medical conditions. This study examines the impact of
synthetic data supplementation, using diffusion models, on the performance of
deep learning (DL) classifiers for CXR analysis. We employed three datasets:
CheXpert, MIMIC-CXR, and Emory Chest X-ray, training conditional denoising
diffusion probabilistic models (DDPMs) to generate synthetic frontal
radiographs. Our approach ensured that synthetic images mirrored the
demographic and pathological traits of the original data. Evaluating the
classifiers' performance on internal and external datasets revealed that
synthetic data supplementation enhances model accuracy, particularly in
detecting less prevalent pathologies. Furthermore, models trained on synthetic
data alone approached the performance of those trained on real data. This
suggests that synthetic data can potentially compensate for real data shortages
in training robust DL models. However, despite promising outcomes, the
superiority of real data persists.

Computer Vision and Pattern Recognition;Machine Learning