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Medical Image Processing based on Generative Adversarial Networks: A Systematic Review.

Jun Liu¹, Kunqi Li¹, Hua Dong¹

¹College of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi, 330063, China.

Current Medical Imaging

|October 26, 2023

Summary

Generative adversarial networks (GANs) enhance medical image analysis by improving data generation for diagnosis and treatment. Future research should address GAN challenges like instability and interpretability.

Keywords:

Computer vision.Deep learning Generative adversarial network Medical image Segmentation Synthesis

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Generative adversarial networks (GANs) offer superior data generation for medical imaging.
GANs are increasingly applied in image reconstruction, segmentation, detection, classification, and cross-modality synthesis.
A review of GANs in medical imaging is beneficial for researchers.

Purpose of the Study:

To review the origin, principles, and extended forms of GANs.
To summarize state-of-the-art GAN-based medical image processing methods.

Main Methods:

Literature search on Google Scholar and PubMed.
Keywords: Segmentation, Classification, medical image, generative adversarial network.
Screening of 5423 publications, with 121 studies included in the final analysis.

Main Results:

Review covers GAN applications from January 1, 2017, to present.
121 studies analyzed across seven areas: synthesis, classification, segmentation, conversion, reconstruction, denoising, and lesion detection.
Studies categorized by clinical applications, classification methods, and imaging modalities.

Conclusions:

GANs effectively address limited training data for medical image diagnostic models.
GANs show promise in advancing medical image augmentation.
Future research should focus on GAN challenges: pattern collapse, instability, and interpretability.