注意力增强的生成对抗网络在医学图像生成领域的研究与应用

doi:10.3969/j.issn.0258-8021.2025.04.010

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Abstract Generative adversarial network (GAN) have been demonstrated great research value and application potential in medical image generation, such as data enhancement and image quality improvement, owing to their excellent generation capabilities. However, traditional GAN models still face core challenges, including insufficient robustness and limited generalization capabilities. To address these issues, attention mechanisms, leveraging their strengths in modeling global feature correlations and focusing on key regions, offer a new technical pathway for enhancing GAN-based medical image generation. Thus, exploring how to efficiently combine attention mechanisms with GAN to boost generation quality has become a research hotspot in areas including denoising, reconstruction, data enhancement, and cross-modal generation. This paper systematically reviewed the advancements in attention-enhanced GAN techniques for medical image generation over the past five years (2019-2024). First, the principles of classical GAN and mainstream attention modules were introduced. Next, from a task-driven perspective, we critically analyzed the improved effects of attention mechanisms on GAN in different tasks. Finally, we delved into the current challenges and proposed potential future research directions. Through multidimensional analysis and discussion, this review hopes to provide valuable insights for advancing technologies in medical dataset expansion and image quality enhancement.

Key words： generative adversarial network attention mechanism medical image generation data augmentation cross-modal generation

Received: 01 April 2025

PACS:

R318

Corresponding Authors: ^*E-mail: lilh@hdu.edu.cn

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	Fan Shanhui
	Liang Shuxin
	Wang Zhiwen
	Wei Kaihua
	Wang Qiang
	Li Lihua

Cite this article:

Fan Shanhui,Liang Shuxin,Wang Zhiwen, et al. Research and Applications of Attention-Enhanced Generative Adversarial Network in MedicalImage Generation[J]. Chinese Journal of Biomedical Engineering, 2025, 44(4): 478-493.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.04.010 OR http://cjbme.csbme.org/EN/Y2025/V44/I4/478

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