Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion

no code implementations • 2 Jan 2024 • Dongxu Li, Jianhao Huang, Chuan Huang, Xiaoqi Qin, Han Zhang, Ping Zhang

For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution.

A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

1 code implementation • 11 Sep 2023 • Linghan Cai, Jianhao Huang, Zihang Zhu, Jinpeng Lu, Yongbing Zhang

However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions.

Computed Tomography (CT) Lesion Segmentation +2

Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme

no code implementations • 27 Feb 2023 • Jianhao Huang, Dongxu Li, Chuan Huang, Xiaoqi Qin, Wei zhang

This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion.

Bayesian Inference Data Compression