Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
This work addresses the need for realistic medical image synthesis in radiology, though it appears incremental by adapting existing diffusion models to a specific domain.
The paper tackled the problem of generating high-resolution chest X-rays by proposing Cheff, a cascaded latent diffusion model that achieves state-of-the-art quality on a 1-megapixel scale, and introduced MaCheX as the largest open collection of chest X-ray datasets to support this synthesis.
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiographs providing state-of-the-art quality on a 1-megapixel scale. We further propose MaCheX, which is a unified interface for public chest datasets and forms the largest open collection of chest X-rays up to date. With Cheff conditioned on radiological reports, we further guide the synthesis process over text prompts and unveil the research area of report-to-chest-X-ray generation.