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GAN-based Data Augmentation for Chest X-ray Classification

2021-07-07 01:36:48
Shobhita Sundaram, Neha Hulkund

Abstract

A common problem in computer vision -- particularly in medical applications -- is a lack of sufficiently diverse, large sets of training data. These datasets often suffer from severe class imbalance. As a result, networks often overfit and are unable to generalize to novel examples. Generative Adversarial Networks (GANs) offer a novel method of synthetic data augmentation. In this work, we evaluate the use of GAN- based data augmentation to artificially expand the CheXpert dataset of chest radiographs. We compare performance to traditional augmentation and find that GAN-based augmentation leads to higher downstream performance for underrepresented classes. Furthermore, we see that this result is pronounced in low data regimens. This suggests that GAN-based augmentation a promising area of research to improve network performance when data collection is prohibitively expensive.

Abstract (translated)

URL

https://arxiv.org/abs/2107.02970

PDF

https://arxiv.org/pdf/2107.02970.pdf


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