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AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis
Huang Z(黄钲)1,2,3; Zhao YW(赵忆文)1,2; Liu YH(刘云会)4,5; Song GL(宋国立)1,2,5
Department机器人学研究室
Source PublicationBiomedical Signal Processing and Control
ISSN1746-8094
2022
Volume72Pages:1-10
Indexed BySCI ; EI
EI Accession number20214611179364
WOS IDWOS:000730090100013
Contribution Rank1
Funding OrganizationNational Key R&D Program of China [Grant No. 2020YFF0305105] ; Natural Science Foundation of China [Grant Nos. 92048203, 62073314 and 61821005] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences [Grant No. 2019205] ; the Program GQRC-19-20, the China Postdoctoral Science Foundation [Grant No. 244716] ; Special Fund for High-level Talents (Shizhen Zhong Team) of the People’s Government of Luzhou-Southwestern Medical University.
KeywordBrain tumor diagnosis Multisequence fusing neural network Magnetic resonance imaging Feature fusion
Abstract

To precisely diagnose the brain tumor types and grades, magnetic resonance imaging (MRI), which is a kind of multisequence imaging technology, is usually applied. However, with the limitations of databases, most current computer-aided brain tumor diagnosis methods employ only a single MRI sequence, and the generalizability of these methods is not ideal. To improve the brain tumor diagnosis performance, an adaptive multisequence fusing neural network (AMF-Net), which can merge the characteristics of different MRI sequences with adaptive weights, is proposed. Inspired by the approximate horizontal symmetry of brains and manual diagnosis process, normalized horizontal differential images are adopted as the spatial attention mechanism, and dense skip connections from T2-weighted (T2-W) sequences are implemented to emphasize the importance of the T2-W sequences. Moreover, to adaptively combine different MRI sequences, an innovative self-learning mechanism, namely adaptive sequence fusion (ASF) module, is proposed. The experimental results show that the average accuracies of the AMF-Net on two databases reach 98.1% and 92.1%, respectively, and the application of the proposed spatial attention mechanism and the ASF module can improve the average accuracy on two databases by 1.7%/1.7% and 1.3%/2.1%, respectively, which indicates that the proposed spatial attention mechanism and the ASF module can improve the performance for brain tumor diagnosis tasks.

Language英语
WOS SubjectEngineering, Biomedical
WOS KeywordDEEP ; CLASSIFICATION ; IMAGES
WOS Research AreaEngineering
Funding ProjectNational Key R&D Program of China[2020YFF0305105] ; Natural Science Foundation of China[92048203] ; Natural Science Foundation of China[62073314] ; Natural Science Foundation of China[61821005] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2019205] ; China Postdoctoral ScienceFoundation[244716] ; Special Fund for High-level Talents (Shizhen Zhong Team) of the People's Government of Luzhou-Southwestern Medical University ; [GQRC-19-20]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29921
Collection机器人学研究室
Corresponding AuthorSong GL(宋国立)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Shengjing Hospital of China Medical University, Shenyang 110011, China
5.Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang 110134, China
Recommended Citation
GB/T 7714
Huang Z,Zhao YW,Liu YH,et al. AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis[J]. Biomedical Signal Processing and Control,2022,72:1-10.
APA Huang Z,Zhao YW,Liu YH,&Song GL.(2022).AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis.Biomedical Signal Processing and Control,72,1-10.
MLA Huang Z,et al."AMF-Net: An adaptive multisequence fusing neural network for multi-modality brain tumor diagnosis".Biomedical Signal Processing and Control 72(2022):1-10.
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