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.
This research was supported by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016).
Y. Xing and Z. Ge—Equal contribution authors.
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Notes
- 1.
R1 is a certified radiologist with 10 years of experience. R2 is a radiology registrar who also has two years of experience with deep learning. To ensure fairness, all of the real and generated images are cropped to remove most of the artefacts and downsized to \(224 \times 224\). The radiologists are not aware of the disease prevalence or the proportion of real and fake images in the test set. The radiologists are asked to work independently to distinguish whether each image is real or fake.
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Xing, Y. et al. (2019). Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_84
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