ArXiv Preprint
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.
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023-03-20
