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Multi-label pathology editing of chest X-rays with a Controlled Diffusion Model.

Huan Chu¹, Xiaolong Qi², Huiling Wang²

¹School of Network Security and Information Technology, Yili Normal University, Yining, 835000, China; Key Laboratory of Intelligent Computing Research and Application, Yining, 835399, China.

Medical Image Analysis

|April 27, 2025

Summary

This study introduces a novel diffusion model for precise, multi-label pathological editing of chest X-ray images. The method uses organ masks and untangling capabilities to maintain original image integrity for clinical use.

Keywords:

Chest X-ray Disentanglement Generative model Pathological modification

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Generative models, specifically diffusion models, are increasingly used for medical image editing.
Current methods struggle with precise multi-label pathological editing in chest X-rays, risking clinical applicability due to image discrepancies.

Purpose of the Study:

To develop a diffusion model capable of accurate multi-label pathological editing of chest X-ray images.
To ensure the integrity of the original thoracic structure during image manipulation.

Main Methods:

A novel diffusion model incorporating untangling capabilities and a mask-based mechanism for bone and organ information.
Utilized a chest X-ray image classifier for feature labels and an organ mask for directed edits.
Proposed algorithm named Chest X-rays_Mpe.

Main Results:

Successfully performed multi-label pathological editing on chest X-ray images.
Maintained the integrity of the original thoracic structure post-editing.
Achieved superior quantitative (MS-SSIM, CLIP scores) and qualitative results compared to existing algorithms, validated by radiology experts.

Conclusions:

The proposed Chest X-rays_Mpe model enables precise and controllable multi-label pathological editing of chest X-rays.
This approach overcomes limitations of current methods, enhancing clinical applicability through preserved image integrity.