Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 60-65.doi: 10.11896/jsjkx.201200072

• Smart Healthcare • Previous Articles     Next Articles

Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning

DU Li-jun1, TANG Xi-lu2, ZHOU Jiao2, CHEN Yu-lan2, CHENG Jian2   

  1. 1 School of Artificial Intelligence,Leshan Normal University,Leshan,Sichuan 614000,China
    2 School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:DU Li-jun,born in 1976,master,lectu-rer.Her main research interests include graphics and images processing,computer vision,and medical image analysis,etc.
    CHENG Jian,born in 1978,professor,Ph.D supervisor.His main research interests include machine learning,computer vision,remote sensing image analysis,medical image analysis,and artificial intelligence,etc.
  • Supported by:
    Open Project Fund of Intelligent Terminal Key Laboratory of Sichuan Province(SCITLAB-0017),Sichuan Science and Technology Program(2020YFG0085,2021YFG0328) and Leshan Science and Technology Program(19GZD044).

Abstract: In recent years,using deep learning to classify Alzheimer's disease has become one of the hotspots in medical image research.But the existing models are difficult to extract medical image features effectively,what's more,the auxiliary information resources of disease classification are wasted.To solve these problems,a classification method of Alzheimer's disease with attention mechanism and multi-task learning based on the deep 3D convolution neural network is proposed.Firstly,using the improved traditional C3D network,a rough low-level feature map is generated.Secondly,this feature map is input into a convolution block with attention mechanism and a common convolution block respectively.The former focuses on the structural characteristics of MRI images,and can obtain the attention weight of different pixel in the feature map,which is multiplied by the output feature map of the latter.Finally,multi-task learning is used to obtain three kinds of outputs by adding different full connected layer.The other two outputs optimize the main classification task through back propagation in the training process.Experimental results show that,compared with the existing classification methods of Alzheimer's disease,the classification accuracy and other indicators of the proposed method on ADNI data set have been improved,which is helpful to promote the follow-up disease classification research.

Key words: 3D convolution, Alzheimer's disease, Attention mechanism, Medical image classification, Multi-task learning

CLC Number: 

  • TP183
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