论文标题
可解释的阿尔茨海默氏病多模式脑成像的卷积卷积网络诊断
Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis
论文作者
论文摘要
与特定神经系统疾病相关的大脑区域的鉴定对于生物标志物和诊断研究至关重要。在本文中，我们提出了一个可解释的图形卷积网络（GCN）框架，用于使用多模式性脑成像数据鉴定和分类阿尔茨海默氏病（AD）。具体而言，我们扩展了梯度类激活映射（GRAD-CAM）技术，以量化GCN从脑连接模式中鉴定出的最歧视性特征。然后，我们利用它们来找到感兴趣的签名区域（ROI），通过检测健康对照区域（HC），轻度认知障碍（MCI）和AD组之间的特征差异。我们在ADNI数据库上使用了来自三种模式的成像数据进行了实验，包括VBM-MRI，FDG-PET和AV45-PET，并表明我们方法学到的ROI特征有效地增强了临床评分预测和疾病状态鉴定的表现。它还成功地识别了与AD和MCI相关的生物标志物。
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.
