Enhancing automated COVID-19 chest X-ray diagnosis by image-to-image GAN translation.
Liang Z, Huang JX, Li J, Chan S
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, Dec 16-19, Seoul, Korea (South), pp. 1068-1071. DOI: 10.1109/BIBM49941.2020.9313466.
Abstract:
The severe pneumonia induced by the infection of the SARS-CoV-2 virus causes massive death in the ongoing COVID-19 pandemic. The early detection of the SARS-CoV-2 induced pneumonia relies on the unique patterns of the chest XRay images. Deep learning is a data-greedy algorithm to achieve high performance when adequately trained. A common challenge for machine learning in the medical domain is the accessibility to properly annotated data. In this study, we apply a conditional adversarial network (cGAN) to perform image to image (Pix2Pix) translation from the non-COVID-19 chest X-Ray domain to the COVID-19 chest X-Ray domain. The objective is to learn a mapping from the normal chest X-Ray visual patterns to the COVID-19 pneumonia chest X-ray patterns. The original dataset has a typical imbalanced issue because it contains only 219 COVID-19 positive images but has 1,341 images for normal chest X-Ray and 1,345 images for viral pneumonia. A U-Net based architecture is applied for the image-to-image translation to generate synthesized COVID-19 X-Ray chest images from the normal chest X-ray images. A 50-convolutional-layer residual net (ResNet) architecture is applied for the final classification task. After training the GAN model for 100 epochs, we use the GAN generator to translate 1,100 COVID-19 images from the normal X-Ray to form a balanced training dataset (3,762 images) for the classification task. The ResNet based classifier trained by the enhanced dataset achieves the classification accuracy of 97.8% compared to 96.1% in the transfer learning mode. When trained with the original imbalanced dataset, the model achieves an accuracy of 96.1% compared to 95.6% in the training from trainby-scratch model. In addition, the classifier trained by the enhanced dataset has more stable measures in precision, recall, and F1 scores across different image classes. We conclude that the GAN-based data enhancement strategy is applicable to most medical image pattern recognition tasks, and it provides an effective way to solve the common expertise dependence issue in the medical domain.
Liang Z, Huang JX, Li J, Chan S. Enhancing automated COVID-19 chest X-ray diagnosis by image-to-image GAN translation.
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, Dec 16-19, Seoul, Korea (South), pp. 1068-1071. DOI: 10.1109/BIBM49941.2020.9313466.
URL: https://ieeexplore.ieee.org/abstract/document/9313466