DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission
This work addresses the critical challenge of enabling timely and effective remote diagnosis and intervention in telehealth by improving medical image transmission, representing a domain-specific advancement.
The paper tackled the problem of transmitting medical images efficiently and robustly over noisy, bandwidth-limited channels for remote healthcare, proposing DiSC-Med, a diffusion-based semantic communication framework that achieved superior reconstruction performance with ultra-high bandwidth efficiency, as validated on real-world medical datasets.
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.