用扩散模型提升医疗影像传输的效率与抗噪能力
DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission
- 基于扩散模型构建语义通信框架，聚焦关键医疗信息
- 在噪声信道下实现超高的带宽效率，重建质量更优
- 适合远程医疗、无线健康监测等实时性要求高的场景
人工智能的快速发展推动了智能医疗与下一代无线通信技术的融合，催生了远程诊断与干预等创新应用。为实现远程医疗的及时响应，如何在带宽有限且存在噪声的信道中高效传输医疗数据成为关键挑战。本文提出一种新型基于扩散模型的语义通信框架DiSC-Med，用于医疗图像传输。该框架通过医学增强压缩模块提升带宽效率，通过去噪模块增强抗干扰能力。相比传统的像素级通信方式，DiSC-Med能有效捕捉关键语义信息，在噪声环境下实现优异的重建性能和超高的带宽效率。在真实医疗数据集上的大量实验验证了该框架的有效性，展现出在鲁棒、高效远程医疗应用中的巨大潜力。
原文摘要 · Abstract (English)
The rapid development of artificial intelligence has driven smart health with next-generation wireless communication technologies, stimulating exciting applications in remote diagnosis and intervention. To enable a timely and effective response for remote healthcare, efficient transmission of medical data through noisy channels with limited bandwidth emerges as a critical challenge. In this work, we propose a novel diffusion-based semantic communication framework, namely DiSC-Med, for the medical image transmission, where medical-enhanced compression and denoising blocks are developed for bandwidth efficiency and robustness, respectively. Unlike conventional pixel-wise communication framework, our proposed DiSC-Med is able to capture the key semantic information and achieve superior reconstruction performance with ultra-high bandwidth efficiency against noisy channels. Extensive experiments on real-world medical datasets validate the effectiveness of our framework, demonstrating its potential for robust and efficient telehealth applications.
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