基于注意力关系网络的无线胶囊内镜图像分类方法  

Wireless Capsule Endoscopy Image Classification Method Based on Attention Relational Network

作　　者：安晨 汪成亮[1] 廖超 肖诗童 AN Chen;WANG Chengliang;LIAO Chao;XIAO Shitong(College of Computer Science,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《计算机工程》2021年第10期252-259,268,共9页Computer Engineering

基　　金：国家自然科学基金(61672115);中央高校基本科研业务费专项资金(2020CDCGJSJ040)。

摘　　要：无线胶囊内镜(WCE)技术可以检测出肠胃道异常,计算机辅助诊断WCE图像方法由于标注图像数据量少、图像类内变异度高和类间相似等原因导致效果不佳。为此,提出一种基于注意力关系网络的WCE图像多分类方法。将关系网络、注意力机制以及元学习训练策略相结合,构造基于注意力机制的嵌入模块以提取WCE图像特征,将提取后的特征进行特征映射级联后输入到关系模块,根据关系模块输出的相似性评分判断样本所属类别,采用元学习训练策略训练网络。实验结果表明,该方法的分类精度高于RelationNet、MAML等小样本分类方法,且在WCE数据集上该方法的精度高达90.28%。Wireless Capsule Endoscope(WCE)technology can detect gastrointestinal abnormalities.However,the performance of computer-aided diagnosis based on WCE images is reduced due to the small amount of labeled image data,intra-class variation and inter-class similarity.To address the problem,an attentional relational network-based WCE image classification method is proposed.The method combines the relational network,the attention mechanism and the meta-learning training strategy.On this basis,an embedded module based on the attention mechanism is built to extract features of WCE images,and then the extracted features are input into the relation module after feature mapping cascade.The category of the samples is judged according to the similarity score output by the relation module,and the network is trained by using the meta-learning training strategy.The experimental results show that the classification accuracy of the proposed method is higher than that of RelationNet,MAML and other small sample classification methods,reaching up to 90.28%on the WCE dataset.

关 键 词：无线胶囊内镜图像 注意力机制 元学习 关系网络 卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]