Kavli Affiliate: Xiang Zhang
| First 5 Authors: Fupei Guo, Fupei Guo, , ,
| Summary:
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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