Generative Adversarial Network in Medical Imaging: A Review

TL;DR

This review discusses how generative adversarial networks (GANs) are increasingly used in medical imaging for tasks like data augmentation, image reconstruction, and cross-modality synthesis, highlighting recent advances and future potential.

Contribution

It provides a comprehensive overview of recent developments in applying GANs to medical imaging, emphasizing their diverse applications and benefits.

Findings

GANs improve data augmentation in medical imaging

GANs enable better image reconstruction and synthesis

Rapid adoption of GANs in various medical imaging tasks

Abstract

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.