Multimodal Representation Learning for Alzheimer's Disease Diagnosis

Alzheimer's disease（AD） is a complex neurodegenerative disease involving a variety of pathogenic factors.So far, the cause of Alzheimer's disease is not clear, the course of the disease is irreversible, and there is no cure.Its early diagnosis and treatment have always been the focus of at...

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Vydáno v:Ji suan ji ke xue Ročník 48; číslo 10; s. 107 - 113
Hlavní autoři: Fan, Lian-xi, Liu, Yan-bei, Wang, Wen, Geng, Lei, Wu, Jun, Zhang, Fang, Xiao, Zhi-tao
Médium: Journal Article
Jazyk:čínština
Vydáno: Chongqing Guojia Kexue Jishu Bu 01.10.2021
Editorial office of Computer Science
Témata:
Algorithms
Alzheimer's disease
alzheimer's disease|disease diagnosis|multimodal fusion|representation learning|autoencoder network
Diagnosis
Diagnostic systems
Learning
Medical diagnosis
Medical imaging
Representations
ISSN:1002-137X
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Shrnutí:Alzheimer's disease（AD） is a complex neurodegenerative disease involving a variety of pathogenic factors.So far, the cause of Alzheimer's disease is not clear, the course of the disease is irreversible, and there is no cure.Its early diagnosis and treatment have always been the focus of attention.The neuroimaging data of subjects has an important auxiliary role in the diagnosis of this disease, and the combination of multimodal data can further improve the diagnostic effect.At present, the multimodal data representation learning of the disease has gradually become an emerging research field, which has attracted wide attention from researchers.An autoencoder based multimodal representation learning method for Alzheimer's disease diagnosis is proposed.Firstly, the multimodal data are initially fused to obtain the primary common representation.Then, it is input into the autoencoder network to learn the final common representation in latent space.Finally, the common representation in latent space is classified to
Bibliografie:ObjectType-Article-1
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ISSN:1002-137X
DOI:10.11896/jsjkx.200900178