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Connectional-style-guided contextual representation learning for brain disease diagnosis

文献类型：期刊论文

作者Wang, Gongshu1; Jiang, Ning1; Ma, Yunxiao1; Chen, Duanduan1; Wu, Jinglong1; Li, Guoqi2; Liang, Dong3; Yan, Tianyi1
刊名NEURAL NETWORKS
出版日期2024-07-01
卷号175页码:14
关键词Transformers Brain disease diagnosis Self-supervised MRI
ISSN号0893-6080
DOI10.1016/j.neunet.2024.106296
通讯作者Chen, Duanduan(duanduan@bit.edu.cn) ; Yan, Tianyi(yantianyi@bit.edu.cn)
英文摘要Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.
WOS关键词CONVOLUTIONAL NEURAL-NETWORKS ; ALZHEIMERS-DISEASE ; GRAY-MATTER ; ORGANIZATION
资助项目STI 2030-Major Projects[2022ZD0208500] ; National Natural Science Foundation of China[U20A20191] ; National Natural Science Foundation of China[62336002] ; National Natural Science Foundation of China[62373056] ; Beijing Natural Science Foundation[Z210012] ; Biological & Medical Engineering Core Facilities, Beijing Institute of Technology
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:001233513000001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构STI 2030-Major Projects ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Biological & Medical Engineering Core Facilities, Beijing Institute of Technology
源URL[http://ir.ia.ac.cn/handle/173211/58461]  
专题数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者Chen, Duanduan; Yan, Tianyi
作者单位1.Beijing Inst Technol, Sch Med Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Chinese Acad Sci, Res Ctr Med AI, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Gongshu,Jiang, Ning,Ma, Yunxiao,et al. Connectional-style-guided contextual representation learning for brain disease diagnosis[J]. NEURAL NETWORKS,2024,175:14.
APA Wang, Gongshu.,Jiang, Ning.,Ma, Yunxiao.,Chen, Duanduan.,Wu, Jinglong.,...&Yan, Tianyi.(2024).Connectional-style-guided contextual representation learning for brain disease diagnosis.NEURAL NETWORKS,175,14.
MLA Wang, Gongshu,et al."Connectional-style-guided contextual representation learning for brain disease diagnosis".NEURAL NETWORKS 175(2024):14.

入库方式： OAI收割

来源：自动化研究所

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