基于显著稀疏强关联的脑功能连接分类方法

针对脑功能连接数据维度过高、冗余特征过多影响神经网络分类准确率的问题，提出一种基于显著稀疏强关联的脑功能连接分类方法. 该方法利用显著特征稀疏模块对原始特征进行筛选增强；采用稀疏强关联特征上下文融合模块对不同感受野内的显著特征信息进行聚合；使用全连接神经网络进行分类预测. 在ABIDE以及ADHD-200数据集上的实验结果表明，所提方法相较于现有的脑功能连接分类算法在准确率上分别提升了10.41%和12.50%. 重要特征的可视化结果表明所提方法能准确定位与疾病相关的脑区，具有一定实际应用价值.

Brain functional connections classification method based on significant sparse strong correlation

A brain functional connectivity classification method based on significant sparse strong correlation was proposed to solve problems that data dimension of brain functional connectivity is too high and many redundant features affect the accuracy of neural network classification. In this method, the salient features sparse module was used to filter and enhance the original features. The sparse strong correlation feature context fusion module was used to aggregate the salient feature information in different receptive fields. The fully connected neural network was used for classification prediction. The results on ABIDE and ADHD-200 datasets showed that the accuracy of brain functional connection classification algorithm was improved by 10.41% and 12.5% respectively compared with the existing brain functional connection classification algorithm. The visualization results of the important features show that the proposed method can accurately locate the brain regions related to disease, which has a certain practical value.