Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs
文献类型：期刊论文
| 作者 | Qiao, Hezhe1,2; Chen, Lin2 ; Ye, Zi3; Zhu, Fan2
|
| 刊名 | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
 |
| 出版日期 | 2021-09-01 |
| 卷号 | 208页码:11 |
| 关键词 | Alzheimer's disease (AD) Contrastive loss Magnetic resonance imaging (MRI) Mini-mental state examination (MMSE) Convolutional neural network (CNN) |
| ISSN号 | 0169-2607 |
| DOI | 10.1016/j.cmpb.2021.106282 |
| 通讯作者 | Zhu, Fan(zhufan@cigit.ac.cn) |
| 英文摘要 | Background and objective: Alzheimer's Disease (AD) is a chronic and fatal neurodegenerative disease with progressive impairment of memory. Brain structural magnetic resonance imaging (sMRI) has been widely applied as important biomarkers of AD. Various machine learning approaches, especially deep learning based models, have been proposed for the early diagnosis of AD and monitoring the disease progression on sMRI data. However, the requirement for a large number of training images still hinders the extensive usage of AD diagnosis. In addition, due to the similarities in human whole-brain structure, finding the subtle brain changes is essential to extract discriminative features from limited sMRI data effectively. Methods: In this work, we proposed two types of contrastive losses with paired sMRIs to promote the diagnostic performance using group categories (G-CAT) and varying subject mini-mental state examination (S-MMSE) information, respectively. Specifically, G-CAT contrastive loss layer was used to learn the closer feature representation from sMRIs with the same categories, while ranking information from S-MMSE assists the model to explore subtle changes between individuals. Results: The model was trained on ADNI-1. Comparison with baseline methods was performed on MIRIAD and ADNI-2. For the classification task on MIRIAD, S-MMSE achieves 93.5% of accuracy, 96.6% of sensitivity, and 94.9% of specificity, respectively. G-CAT and S-MMSE both reach remarkable performance in terms of classification sensitivity and specificity respectively. Comparing with state-of-the-art methods, we found this proposed method could achieve comparable results with other approaches. Conclusion: The proposed model could extract discriminative features under whole-brain similarity. Extensive experiments also support the accuracy of this model, i.e., it provides better ability to identify uncertain samples, especially for the classification task of subjects with MMSE in 22-27. Source code is freely available at https://github.com/fengduqianhe/ADComparative . (c) 2021 Elsevier B.V. All rights reserved. |
| 资助项目 | National Nature Sci-ence Foundation of China[61802360] ; National Nature Sci-ence Foundation of China[61902370] ; UK Alzheimer's Society[RF116] ; GlaxoSmithKline[6GKC] |
| WOS研究方向 | Computer Science ; Engineering ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000685504200013 |
| 出版者 | ELSEVIER IRELAND LTD |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13897]  |
| 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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 3.Johns Hopkins Univ, Baltimore, MD 21218 USA |
| 推荐引用方式 GB/T 7714 | Qiao, Hezhe,Chen, Lin,Ye, Zi,et al. Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2021,208:11. |
| APA | Qiao, Hezhe,Chen, Lin,Ye, Zi,&Zhu, Fan.(2021).Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,208,11. |
| MLA | Qiao, Hezhe,et al."Early Alzheimer's disease diagnosis with the contrastive loss using paired structural MRIs".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 208(2021):11. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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