论文标题
可解释的CNN注意力网络（C-意见网络）自动检测阿尔茨海默氏病
Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
论文作者
论文摘要
在这项工作中，我们提出了三种可解释的深度学习体系结构，以根据他们的语言能力自动检测患有阿尔茨海默氏病的患者。体系结构使用：（1）仅语音功能； （2）仅通过统一体系结构来嵌入功能的语言和（3）这两个功能类。我们使用自我发挥的机制和可解释的一维卷积掌网（CNN）来生成模型动作的两种解释：阶层内解释和类间的解释。类间的解释捕获了该类别中每个不同特征的相对重要性，而类间的解释捕获了类之间的相对重要性。请注意，尽管我们在本文中考虑了两类功能，但由于其模块化，该体系结构很容易扩展到更多类。广泛的实验和与最近的几个模型的比较表明，我们的方法的精度为92.2％，F1得分为0.952ON，在dementiabank数据集中胜过这些方法，同时能够生成解释。我们通过示例显示了如何使用注意值生成这些解释。
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional ConvolutionalNeural Network (CNN) to generate two types of explanations of the model`s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentation and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.
