Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation

TL;DR

This paper introduces a novel GAN-based method for generating high-quality, plausible chest X-ray images with preserved pathology features, enhancing data augmentation for lung disease recognition.

Contribution

A new image-to-image translation approach that maintains original pathology regions while generating diverse lung features, leveraging both annotated and unannotated images.

Findings

Radiologists rated generated images as high quality.

Data augmentation improved disease localization performance.

Model outperformed existing generative approaches.

Abstract

Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to chest X-ray data augmentation for lung disease recognition. However, the implausible and distorted pathology features generated from the less than perfect generator may lead to wrong clinical decisions. Why not keep the original pathology region? We proposed a novel approach that allows our generative model to generate high quality plausible images that contain undistorted pathology areas. The main idea is to design a training scheme based on an image-to-image translation network to introduce variations of new lung features around the pathology ground-truth area. Moreover, our model is able to leverage both annotated disease images and unannotated healthy lung images for the purpose of generation. We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.