Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning

doi:10.1016/j.ins.2020.09.012

	Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning
	Li, Dan1,2,3 ; Du, Changde1,2,3,4 ; Wang, Shengpei1,2,3 ; Wang, Haibao1,2,3 ; He, Huiguang1,2,3,5
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
	2021-02-08
卷号	547 页码:1025-1044
通讯作者	He, Huiguang(huiguang.he@ia.ac.cn)
摘要	Functional magnetic resonance imaging (fMRI) is widely used in the field of brain semantic decoding. However, as fMRI data acquisition is time-consuming and expensive, the number of samples is usually small in the existing fMRI datasets. It is difficult to build an accurate brain decoding model for a subject with insufficient fMRI data. The majority of semantic decoding methods focus on designing predictive model with limited samples, while less attention is paid to fMRI data augmentation. Leveraging data from related but different subjects can be regarded as a new strategy to improve the performance of predictive model. There are two challenges when using information from different subjects: 1) feature mismatch; 2) distribution mismatch. In this paper, we propose a multi-subject fMRI data augmentation method to address the above two challenges, which can improve the decoding accuracy of the target subject. Specifically, the subject information can be translated from one to another by using multiple subject-specific encoders, decoders and discriminators. The encoder maps each subject to a shared latent space, solving the feature mismatch problem. The decoders and discriminators form multiple generative adversarial network architectures, which solves the distribution mismatch problem. Meanwhile, to ensure that the representation of the latent space preserves information of the input space, our method not only minimizes the local data reconstruction loss, but also preserves the sparse reconstruction (semantic) relation over the whole dataset of the input space. Extensive experiments on three fMRI datasets demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.
关键词	Data augmentation Semantic decoding Multi-view adversarial learning Sparse reconstruction relation
DOI	10.1016/j.ins.2020.09.012
关键词[WOS]	PATTERN-ANALYSIS ; VOXEL SELECTION ; IMAGES
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundationof China[61976209] ; National Natural Science Foundationof China[62020106015] ; CAS Inte rnational Collaboration Key Project[173211KYSB20190024] ; Strategic Priority Research Program of CAS[XDB32040000]
项目资助者	National Natural Science Foundationof China ; CAS Inte rnational Collaboration Key Project ; Strategic Priority Research Program of CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000590678700002
出版者	ELSEVIER SCIENCE INC
七大方向——子方向分类	人工智能基础理论
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42681
专题	脑图谱与类脑智能实验室_神经计算与脑机交互
通讯作者	He, Huiguang
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Huawei Cloud BU EI Innovat Lab, Beijing 100085, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
通讯作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Li, Dan,Du, Changde,Wang, Shengpei,et al. Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning[J]. INFORMATION SCIENCES,2021,547:1025-1044.
APA	Li, Dan,Du, Changde,Wang, Shengpei,Wang, Haibao,&He, Huiguang.(2021).Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning.INFORMATION SCIENCES,547,1025-1044.
MLA	Li, Dan,et al."Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning".INFORMATION SCIENCES 547(2021):1025-1044.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Multi-subject data a（2428KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载