摘要
智能诊断在阿尔茨海默病(AD)的诊断中已得到广泛研究,但已有的智能建模方法还不能充分利用多模态的数据信息,以至于在病程早期阶段的诊断中出现识别精确度不高的问题。为提高阿尔茨海默病及其早期阶段智能诊断的效果,提出一种兼顾个性特征和融合特征的阿尔茨海默病分类方法。首先使用超图卷积网络(HGCN)对MRI、PET和CSF三个模态的数据分别进行特征提取,以获得每个模态的高阶深度特征。同时通过低秩多模态融合对这三个模态的数据进行特征融合,以获得多个模态之间的隐藏关联特征。最后通过一个多视角分类器对以上获取的特征进行综合分类。利用ADNI数据集对阿尔茨海默病进行多组任务分类,以验证所提方法。与其他先进方法相比,该方法在保证AD阶段分类效果的情况下,有效提高了病程早期阶段的分类精度。
Intelligence diagnosis has been widely studied in the diagnosis of Alzheimer’s disease(AD),but existing intelligent modeling methods cannot make full use of the multi-modal data feature information.As a result,the recognition accuracy is not high in the diagnosis of disease in the early stage.In order to improve the diagnosis effect of AD and its early stage,a classification method of Alzheimer’s disease integrating individual feature and fusion feature is proposed.Firstly,the hypergraph convolutional network(HGCN)is used to extract features from the data of three modalities of MRI(magnetic resonance imaging),PET(positron emission computed tomography),and CSF(cerebro-spinal fluid)to obtain the high-order deep feature.At the same time,the data of these three modalities are fused through low-rank multimodal fusion to obtain hidden correlation features among multiple modalities.Finally,a multi-view classifier is used to comprehensively classify the above-obtained features.The ADNI dataset is used to classify AD in multiple groups of tasks to verify the proposed method.Compared with other state-of-the-art methods,the proposed method effectively improves the classification accuracy in the early stage of the disease while ensuring the classification effect of the AD stage.
作者
曹营利
邓赵红
胡曙东
王士同
CAO Yingli;DENG Zhaohong;HU Shudong;WANG Shitong(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122,China;Affiliated Hospital of Jiangnan University,Wuxi,Jiangsu 214062,China)
出处
《计算机科学与探索》
CSCD
北大核心
2023年第7期1658-1668,共11页
Journal of Frontiers of Computer Science and Technology
基金
江苏省杰出青年基金项目(BK20140001)
国家自然科学基金面上项目(61772239)。
