GAN-based Data Augmentation for Chest X-ray Classification

Shobhita Sundaram; Neha Hulkund

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arXiv · 2107.02970

GAN-based Data Augmentation for Chest X-ray Classification

Shobhita Sundaram·Neha Hulkund

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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.

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BibTeX RIS

Shobhita Sundaram, Neha Hulkund. 2021-07-07. GAN-based Data Augmentation for Chest X-ray Classification. https://arxiv.org/abs/2107.02970

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