Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs

Image augmentation is a commonly performed technique to prevent class imbalance in datasets to compensate for insufficient training samples, or to prevent model overfitting. Traditional augmentation (TA) techniques include various image transformations, such as rotation, translation, channel splitting, etc. Alternatively, Generative Adversarial Network (GAN), due to its proven ability to synthesize convincingly-realistic images, has been used to perform image augmentation as well. However, it is unclear whether GAN augmentation (GA) strategy provides an advantage over TA for medical image classification tasks. In this paper, we study the usefulness of TA and GA for classifying abnormal chest X-ray (CXR) images. We first trained a progressive-growing GAN (PG-GAN) to synthesize high-resolution CXRs for performing GA. Then, we trained an abnormality classifier using three training sets individually - training set with TA, with GA and with no augmentation (NA). Finally, we analyzed the abnormality classifier's performance for the three training cases, which led to the following conclusions: (1) GAN strategy is not always superior to TA for improving the classifier's performance; (2) in comparison to NA, however, both TA and GA leads to a significant performance improvement; and, (3) increasing the quantity of images in TA and GA strategies also improves the classifier's performance.