Medical segmentation is a task that pays attention to details. The rapid development of deep learning in image processing technology makes it possible to segment objects accurately on small datasets. In this paper, we propose a hierarchical multi-scale attention network that focuses on the fine-grained parts of the target. Our attention network consists of a hierarchical encoder module with dense connections, a multi-scale module attention to fine-grained parts, and a decoder module. We also combine the weighted cross-entropy loss function based on details and the Dice coefficient loss to increase the sensitivity of fine grains. To verify our module’s performance, we carried out a series of comparative experiments on the multi-scale attention module on the DRIVE dataset. We determine the best structure through experiments and compare it with several classical deep learning models. Our experiments show that extracting semantic information of images at an appropriate resolution can also improve the accuracy of detail segmentation. To show the generalization ability of the work, we conducted experiments on different DRIVE, STARE, and CHASE_DB 1 datasets, and our method achieved 0.8802/0.8464/0.8216 in sensitivity performance metric, 0.9756/0.9869/0.9784 in specificity, and 0.9675/0.9657/0.9637 in accuracy.

医学细分是一项注重细节的任务。深度学习技术在图像处理技术中的迅速发展，使得在小型数据集上准确地分割对象成为可能。在本文中，我们提出了一个层次化的多尺度注意力网络，重点关注目标的细粒度部分。我们的关注网络包括一个具有密集连接的分层编码器模块，一个关注细粒度部分的多尺度模块以及一个解码器模块。我们还将基于细节的加权交叉熵损失函数与Dice系数损失相结合，以提高细晶粒的敏感性。为了验证我们模块的性能，我们在DRIVE数据集的多尺度注意力模块上进行了一系列比较实验。我们通过实验确定最佳结构，并将其与几种经典的深度学习模型进行比较。我们的实验表明，以适当的分辨率提取图像的语义信息还可以提高细节分割的准确性。为了显示这项工作的概括能力，我们在不同的DRIVE，STARE和CHASE_DB 1数据集上进行了实验，我们的方法在灵敏度性能指标上达到0.8802 / 0.8464 / 0.8216，在特异性上达到0.9756 / 0.9869 / 0.9784，在0.9675 / 0.9657 /精度为0.9637。