Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

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成果类型：

期刊论文

作者：

Huang, Gexin;Liu, Ke;Liang, Jiawen;Cai, Chang;Gu, Zheng Hui;...

作者机构：

[Huang, Gexin; Gu, Zheng Hui; Li, Yuanqing; Yu, Zhu Liang] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510641, Peoples R China.

[Liu, Ke] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China.

[Liang, Jiawen] South China Univ Technol, Sch Intelligent Engn, Guangzhou 510641, Peoples R China.

[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.

[Qi, Feifei] Guangdong Univ Finance, Sch Internet Finance & Informat Engn, Guangzhou 510521, Peoples R China.

语种：

英文

关键词：

Training;Spatiotemporal phenomena;Electromagnetics;Deep learning;Convolution;Magnetic resonance imaging;Inverse problems;Convolutional encoder-decoder network (CedNet);data synthesis;deep learning;electromagnetic source imaging (ESI)

期刊：

IEEE Transactions on Neural Networks and Learning Systems

ISSN：

2162-237X

年：

2022

卷：

页码：

1-15

DOI：

10.1109/TNNLS.2022.3209925

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61876063, 62276102, 61906048, 61703065, 62277023 and 62007013) Technology Innovation 2030 (Grant Number: 2022ZD0211700) Hubei Provincial Natural Science Foundation of China (Grant Number: 2021CFB384)

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本校为其他机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn...

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Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

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