Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation
This work addresses the need for reliable data augmentation in medical imaging to enhance disease diagnosis, though it is incremental as it builds on existing GAN-based approaches.
The paper tackled the problem of generating plausible chest X-ray images for data augmentation in lung disease recognition by proposing a method that preserves undistorted pathology areas, resulting in high-quality synthetic images validated by radiologists and improved disease localization performance.
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.