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Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points

文献类型：期刊论文

作者Qiao, Hezhe1,2; Chen, Lin1; Zhu, Fan1
刊名COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
出版日期2022
卷号213页码:10
关键词Alzheimer's Disease (AD) Convolutional Neural Network (CNN) Magnetic Resonance Imaging (MRI) Mini-Mental State Examination (MMSE) Ranking learning
ISSN号0169-2607
DOI10.1016/j.cmpb.2021.106503
通讯作者Zhu, Fan(zhufan@cigit.ac.cn)
英文摘要Background and objective: Alzheimer's disease (AD) is a fatal neurodegenerative disease. Predicting Mini mental state examination (MMSE) based on magnetic resonance imaging (MRI) plays an important role in monitoring the progress of AD. Existing machine learning based methods cast MMSE prediction as a single metric regression problem simply and ignore the relationship between subjects with various scores. Methods: In this study, we proposed a ranking convolutional neural network (rankCNN) to address the prediction of MMSE through muti-classification. Specifically, we use a 3D convolutional neural network with sharing weights to extract the feature from MRI, followed by multiple sub-networks which transform the cognitive regression into a series of simpler binary classification. In addition, we further use a ranking layer to measure the ranking information between samples to strengthen the ability of the classification by extracting more discriminative features. Results: We evaluated the proposed model on ADNI-1 and ADNI-2 datasets with a total of 1,569 subjects. The Root Mean Squared Error (RMSE) of our proposed model at baseline is 2 . 238 and 2 . 434 on ADNI-1 and ADNI-2, respectively. Extensive experimental results on ADNI-1 and ADNI-2 datasets demonstrate that our proposed model is superior to several state-of-theart methods at both baseline and future MMSE prediction of subjects. Conclusion: This paper provides a new method that can effectively predict the MMSE at baseline and future time points using baseline MRI, making it possible to use MRI for accurate early diagnosis of AD. The source code is freely available at https://github.com/fengduqianhe/ADrankCNN-master . (c) 2021 Elsevier B.V. All rights reserved.
资助项目National Nature Sci-ence Foundation of China[61902370] ; National Nature Sci-ence Foundation of China[61802360] ; Chongqing Research Program of Tech-nology Innovation and Application[cstc2019jscx-zdztzxX0019] ; key cooperation project of chongqing municipal education commission[HZ2021008]
WOS研究方向Computer Science ; Engineering ; Medical Informatics
语种英语
WOS记录号WOS:000720347300011
出版者ELSEVIER IRELAND LTD
源URL[http://119.78.100.138/handle/2HOD01W0/14556]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Zhu, Fan
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Qiao, Hezhe,Chen, Lin,Zhu, Fan. Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,213:10.
APA Qiao, Hezhe,Chen, Lin,&Zhu, Fan.(2022).Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,213,10.
MLA Qiao, Hezhe,et al."Ranking convolutional neural network for Alzheimer's disease mini-mental state examination prediction at multiple time-points".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 213(2022):10.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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