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

  • Yunyan XingEmail author
  • Zongyuan GeEmail author
  • Rui Zeng
  • Dwarikanath Mahapatra
  • Jarrel Seah
  • Meng Law
  • Tom Drummond
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


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.

Supplementary material

490281_1_En_84_MOESM1_ESM.pdf (705 kb)
Supplementary material 1 (pdf 704 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yunyan Xing
    • 1
    Email author
  • Zongyuan Ge
    • 1
    Email author
  • Rui Zeng
    • 1
  • Dwarikanath Mahapatra
    • 2
  • Jarrel Seah
    • 1
  • Meng Law
    • 1
  • Tom Drummond
    • 1
  1. 1.Monash UniversityMelbourneAustralia
  2. 2.IBM ResearchMelbourneAustralia

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