Generative Adversarial Networks for Data Augmentation

TL;DR

This paper discusses how Generative Adversarial Networks (GANs) can be used to augment medical datasets by generating realistic synthetic data, addressing data scarcity and ethical concerns in medical imaging.

Contribution

It reviews the application of GANs for data augmentation in medical imaging and highlights ongoing challenges in ensuring high-quality synthetic data for clinical use.

Findings

GANs can generate realistic medical images for data augmentation

Synthetic data helps improve AI model performance in medical tasks

Challenges remain in validating the clinical quality of generated images

Abstract

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.