联营
计算机科学
神经影像学
人工智能
分类器(UML)
机器学习
阿尔茨海默病神经影像学倡议
模式识别(心理学)
深度学习
认知障碍
认知
心理学
神经科学
作者
Wen Yu,Baiying Lei,Michael K. Ng,Albert C. Cheung,Yanyan Shen,Shuqiang Wang
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2021-03-17
卷期号:33 (9): 4945-4959
被引量:107
标识
DOI:10.1109/tnnls.2021.3063516
摘要
It is of great significance to apply deep learning for the early diagnosis of Alzheimer’s disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess mild cognitive impairment (MCI) and AD. By tensorizing a three-player cooperative game-based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of holistic magnetic resonance imaging (MRI). To the best of our knowledge, the proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis. Extensive experimental results on Alzheimer’s disease neuroimaging initiative (ADNI) data set are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semisupervised learning purpose.
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