  The availability of large-scale chest X-ray datasets is a requirement for
developing well-performing deep learning-based algorithms in thoracic
abnormality detection and classification. However, biometric identifiers in
chest radiographs hinder the public sharing of such data for research purposes
due to the risk of patient re-identification. To counteract this issue,
synthetic data generation offers a solution for anonymizing medical images.
This work employs a latent diffusion model to synthesize an anonymous chest
X-ray dataset of high-quality class-conditional images. We propose a
privacy-enhancing sampling strategy to ensure the non-transference of biometric
information during the image generation process. The quality of the generated
images and the feasibility of serving as exclusive training data are evaluated
on a thoracic abnormality classification task. Compared to a real classifier,
we achieve competitive results with a performance gap of only 3.5% in the area
under the receiver operating characteristic curve.
