Attention-based generative adversarial network in medical imaging: A narrative review

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Computer Vision

Background:

• Generative adversarial networks (GANs) are powerful probabilistic models widely used in image processing.
• GAN applications in medical imaging face challenges, prompting interest in attention mechanisms.
• Transformer architectures with self-attention enhance representation learning and capture long-range dependencies.

Purpose of the Study:

• To review recent advances in transformer-based GANs for medical image analysis.
• To summarize applications in medical image segmentation, synthesis, and detection.
• To highlight the potential of attention mechanisms in GANs for medical diagnosis.

Main Methods:

• Review of recent literature on transformer-based GANs and attention mechanisms in medical imaging.
• Analysis of techniques combining attention modules with adversarial training schemes.
• Examination of applications in segmentation, synthesis, and detection tasks.

Main Results:

• Attention modules effectively integrated into GANs improve lesion detection and feature extraction.
• Transformer-based GANs demonstrate promise for precise medical image processing and diagnosis.
• Studies show attention-based GANs are efficient tools for medical image analysis.

Conclusions:

• Research on GANs and attention mechanisms in medical imaging is emerging but holds significant potential.
• Attention-based generative adversarial networks represent a promising computational model for future medical image analysis.
• These models offer advancements for research and clinical applications in medical image processing and diagnosis.