糖尿病视网膜病变分期双分支混合注意力决策网络

Dual-branch hybrid attention decision net for diabetic retinopathy classification

为了缓解大规模糖尿病视网膜病变(DR)筛查需求下医疗资源不足的问题,本文提出了糖尿病视网膜病变分期双分支混合注意力决策网络(BiRAD-Net)。该网络分为特征提取和分类两个阶段:在特征提取阶段,引入混合注意力机制抑制噪声,并设计了特征分级决策网络进一步优化特征质量;在特征分类阶段,设计了双分支分类器以及对应的损失,以减缓标签数据不足带来的影响,增强分类准确性。此外,在模型训练过程中应用迁移学习技术来提高模型的精度、降低训练所需数据量。在KAGGLE数据集上的实验结果表明:本文方法对糖尿病视网膜病变的各个阶段均具有较好的诊断能力,优于其他对比方法。

In order to address the challenges caused by limited medical resources in large-scale Diabetic Retinopathy(DR)screening process,a dual-branch hybrid attention decision net(BiRAD-Net)for DR classification is proposed. The proposed network consists of feature extraction and classification two stages.In the feature extraction stage,a hybrid attention is introduced to suppress the noise,and feature grade decision network is designed to improve feature quality. In the feature classification stage,dual-branch classifiers and corresponding dual-branch loss are designed to alleviate the impact of insufficient data with five-category labels and enhance the classification accuracy. Furthermore,the transfer learning technology is applied in the training process,together with the above steps,to improve the accuracy of model and reduce the amount of training data. Experimental results on KAGGLE dataset indicate that BiRAD-Net shows excellent diagnostic ability for all the stages of DR and preforms better than other existing comparison methods.