# GAN-based Data Augmentation for Chest X-ray Classification

arXiv: https://arxiv.org/abs/2107.02970 · HTML: https://mlsanity.com/paper/2107.02970/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Disentangling Adversarial Robustness and Generalization](https://mlsanity.com/paper/1812.00740/) (2018)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [On the limits of cross-domain generalization in automated X-ray prediction](https://mlsanity.com/paper/2002.02497/) (2020)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI](https://mlsanity.com/paper/2004.10734/) (2020)
- [Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels](https://mlsanity.com/paper/1911.06475/) (2019)

## Built on by (most cited first, 18 of 18)

- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Diffusion Probabilistic Models beat GANs on Medical Images](https://mlsanity.com/paper/2212.07501/) (2022)
- [Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis](https://mlsanity.com/paper/2303.11224/) (2023)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance](https://mlsanity.com/paper/2301.08479/) (2023)
- [Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection](https://mlsanity.com/paper/2109.00891/) (2021)
- [Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records](https://mlsanity.com/paper/2409.07012/) (2024)
- [Transforming Image Generation from Scene Graphs](https://mlsanity.com/paper/2207.00545/) (2022)
- [Hyperparameter Optimization for COVID-19 Chest X-Ray Classification](https://mlsanity.com/paper/2201.10885/) (2022)
- [Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2205.09842/) (2022)
- [Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification](https://mlsanity.com/paper/2307.15987/) (2023)
- [A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage](https://mlsanity.com/paper/2408.00940/) (2024)
- [Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques](https://mlsanity.com/paper/2409.04424/) (2024)
- [Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Images](https://mlsanity.com/paper/2409.20122/) (2024)
- [Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Models](https://mlsanity.com/paper/2603.15525/) (2026)
- [A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rays](https://mlsanity.com/paper/2609.06276/) (2026)
- [FleXray: Universal Clinical X-ray Segmentation](https://mlsanity.com/paper/2609.26756/) (2026)

## Abstract (arXiv)

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.