论文标题
视网膜分割的残留空间注意网络
Residual Spatial Attention Network for Retinal Vessel Segmentation
论文作者
论文摘要
可靠的视网膜血管分割可以用作监测和诊断某些疾病（例如糖尿病和高血压）的一种方式，因为它们会影响视网膜血管结构。在这项工作中，我们提出了剩余的空间注意网络（RSAN）进行视网膜血管分割。 RSAN采用了修改的残差结构，该结构集成了Dropblock，不仅可以用来构建深层网络来提取更复杂的血管特征，而且还可以有效地减轻过度拟合。此外，为了进一步提高网络的表示能力，基于此修改后的残留块，我们引入了空间注意力（SA），并提出残留的空间注意块（RSAB）来构建RSAN。我们采用公共驱动器并追逐DB1彩色图像数据集来评估拟议的RSAN。实验表明，经过修改的残留结构和空间注意力在这项工作中有效，我们拟议的RSAN实现了最先进的性能。
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascular structure. In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation. RSAN employs a modified residual block structure that integrates DropBlock, which can not only be utilized to construct deep networks to extract more complex vascular features, but can also effectively alleviate the overfitting. Moreover, in order to further improve the representation capability of the network, based on this modified residual block, we introduce the spatial attention (SA) and propose the Residual Spatial Attention Block (RSAB) to build RSAN. We adopt the public DRIVE and CHASE DB1 color fundus image datasets to evaluate the proposed RSAN. Experiments show that the modified residual structure and the spatial attention are effective in this work, and our proposed RSAN achieves the state-of-the-art performance.
