mlsanityData to 14 Oct 2026

GAN-based Data Augmentation for Chest X-ray Classification

arXiv 2107.02970 ↗First posted 7 Jul 2021eess.IV cs.CV cs.LGcited by 18

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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.

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

10 of 32 references.

References · 10

  1. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  2. 2020Overfitting in adversarially robust deep learningcited by 506
  3. 2017Data Augmentation Generative Adversarial Networkscited by 278
  4. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 231
  5. 2018Disentangling Adversarial Robustness and Generalizationcited by 172
  6. 2018BAGAN: Data Augmentation with Balancing GANcited by 90
  7. 2020On the limits of cross-domain generalization in automated X-ray predictioncited by 62
  8. 2020Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labelscited by 56
  9. 2020Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRIcited by 17
  10. 2019Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labelscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 3; 2024: 5; 2025: 0; 2026: 3.

18 papers.

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  1. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  2. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  3. 2022Diffusion Probabilistic Models beat GANs on Medical Imagescited by 77
  4. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesiscited by 32
  5. 2024Physics-Inspired Generative Models in Medical Imaging: A Reviewcited by 5
  6. 2023Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalancecited by 3
  7. 2021Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detectioncited by 2
  8. 2024Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Recordscited by 2
  9. 2022Transforming Image Generation from Scene Graphscited by 1
  10. 2022Hyperparameter Optimization for COVID-19 Chest X-Ray Classificationcited by 0
  11. 2022Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networkscited by 0
  12. 2023Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classificationcited by 0
6 more
  1. 2024A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhagecited by 0
  2. 2024Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniquescited by 0
  3. 2024Training a Computer Vision Model for Commercial Bakeries with Primarily Synthetic Imagescited by 0
  4. 2026Clinically Aware Synthetic Image Generation for Concept Coverage in Chest X-ray Modelscited by 0
  5. 2026A Comparative Study of GAN-Based Deep Learning Models for Pneumonia Detection in Chest X-Rayscited by 0
  6. 2026FleXray: Universal Clinical X-ray Segmentationcited by 0