Cascaded Latent Diffusion Models for High-Resolution Chest X-Ray Synthesis

MCML Authors

Tobias Weber

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Michael Ingrisch

Prof. Dr.

Principal Investigator

Clinical Data Science in Radiology

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

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.

inproceedings

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PAKDD 2023

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023.

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Authors

T. Weber • M. Ingrisch • B. Bischl • D. Rügamer

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Links

DOI

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Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine