论文标题
全面监督的注意力网络，用于分割Covid-19病变
Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions
论文作者
论文摘要
肺CT扫描的COVID-19病变的自动描绘有助于患者的诊断和预后。受感染区域的不对称形状和定位使任务极为困难。在多个尺度上捕获信息将有助于在全球和本地层面上解密特征，以涵盖可变大小和纹理的病变。我们介绍了全面监督的注意力网络（FUDSA-NET），以有效地分割CT图像中电晕感染的肺部区域。该模型考虑了来自编码路径的所有级别的激活响应，其中包括在网络不同级别上获得的多量表功能。这有助于分段目标区域（病变）的形状，大小和对比度。将整个多量表特征的整个范围纳入新的注意机制有助于优先选择活化反应和包含有用信息的位置。通过深入的监督，可以促进沿解码器路径确定强大和歧视性特征。重塑解码器臂中的连接以处理消失的梯度问题。从实验结果中可以看出，Fudsa-net超过了其他最先进的体系结构。特别是在表征复杂的病变几何形状时。
Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassing multi-scalar features acquired at different levels of the network. This helps segment target regions (lesions) of varying shape, size and contrast. Incorporation of the entire gamut of multi-scalar characteristics into the novel attention mechanism helps prioritize the selection of activation responses and locations containing useful information. Determining robust and discriminatory features along the decoder path is facilitated with deep supervision. Connections in the decoder arm are remodeled to handle the issue of vanishing gradient. As observed from the experimental results, FuDSA-Net surpasses other state-of-the-art architectures; especially, when it comes to characterizing complicated geometries of the lesions.
