Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation
Autor: | Rui Zeng, Yunyan Xing, Jarrel Seah, Tom Drummond, Zongyuan Ge, Dwarikanath Mahapatra, Meng Law |
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Rok vydání: | 2019 |
Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Disease 010501 environmental sciences medicine.disease 01 natural sciences 03 medical and health sciences Generative model Adversarial system ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Lung disease X ray data medicine Leverage (statistics) Pairwise comparison 030212 general & internal medicine Artificial intelligence Pulmonary pathology business 0105 earth and related environmental sciences |
Zdroj: | Lecture Notes in Computer Science ISBN: 9783030322250 |
Popis: | 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. |
Databáze: | OpenAIRE |
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