GAN-based Data Augmentation for Chest X-ray Classification

• URL: http://arxiv.org/abs/2107.02970v1
• Date: Wed, 7 Jul 2021 01:36:48 GMT
• Title: GAN-based Data Augmentation for Chest X-ray Classification
• Authors: Shobhita Sundaram and Neha Hulkund
• Abstract summary: Generative Adrialversa Networks (GANs) offer a novel method of synthetic data augmentation. GAN-based augmentation leads to higher downstream performance for underrepresented classes. This suggests that GAN-based augmentation a promising area of research to improve network performance when data collection is prohibitively expensive.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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