IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4945-4959. doi: 10.1109/TNNLS.2021.3063516. Epub 2022 Aug 31.
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.
将深度学习应用于阿尔茨海默病（AD）的早期诊断具有重要意义。在这项工作中，提出了一种新的基于张量高阶池化的生成对抗网络（GAN），用于评估轻度认知障碍（MCI）和 AD。通过张量化基于三方合作博弈的框架，所提出的模型可以受益于大脑的结构信息。通过将高阶池化方案纳入分类器，所提出的模型可以充分利用整体磁共振成像（MRI）的二阶统计信息。据我们所知，所提出的基于张量分解、高阶池化和半监督学习的生成对抗网络（THS-GAN）是首次用于 AD 诊断的 MR 图像分类的工作。报告了对阿尔茨海默病神经影像学倡议（ADNI）数据集的广泛实验结果，以证明所提出的 THS-GAN 与现有方法相比具有优越的性能，并表明张量分解和高阶池化都可以增强分类性能。生成样本的可视化也表明，所提出的模型可以为半监督学习目的生成合理的样本。
