论文标题
海网：糖尿病性视网膜病等级的挤压和激发注意网
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
论文作者
论文摘要
糖尿病是个体中最常见的疾病之一。 \ textit {糖尿病性视网膜病}（DR）是糖尿病的并发症，可能导致失明。基于视网膜图像的自动DR分级为治疗计划提供了巨大的诊断和预后价值。但是，严重程度之间的细微差异使得使用常规方法很难捕获重要特征。为了减轻问题，提出了一种新的针对强大的DR分级的深度学习架构，称为海网，其中，空间注意力和渠道关注也可以互相提高并相互提高，从而提高了分类性能。另外，提出了混合损失函数，以进一步最大化阶层间距离并降低阶层内变异性。实验结果表明了所提出的体系结构的有效性。
Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods. To alleviate the problems, a new deep learning architecture for robust DR grading is proposed, referred to as SEA-Net, in which, spatial attention and channel attention are alternatively carried out and boosted with each other, improving the classification performance. In addition, a hybrid loss function is proposed to further maximize the inter-class distance and reduce the intra-class variability. Experimental results have shown the effectiveness of the proposed architecture.
