论文标题
DSNET：一个简单而有效的网络，具有双流关注的病变分割
DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation
论文作者
论文摘要
病变细分需要速度和准确性。在本文中，我们提出了一个简单而有效的网络DSNET，该网络由基于变压器和基于卷积神经网络（CNN）基于卷积的不同金字塔解码器组成，其中包含三个双流动（DSA）模块。具体而言，DSA模块通过假阳性流（FPSA）分支和假阴道注意（FNSA）分支从两个相邻级别的特征融合了功能，以获得具有多样化的上下文信息的功能。我们将我们的方法与各种最新的（SOTA）病变细分方法与几个公共数据集进行了比较，包括CVC-ClinicDB，Kvasir-Seg和ISIC-2018任务1。实验结果表明，我们的方法表明，我们的方法可以在平均固定杆系数（MDICE）和平均值（MISINECTECE）方面实现SOTA的性能，并与平均值（MDICE）和MIIINESCENTINCE相关。
Lesion segmentation requires both speed and accuracy. In this paper, we propose a simple yet efficient network DSNet, which consists of a encoder based on Transformer and a convolutional neural network(CNN)-based distinct pyramid decoder containing three dual-stream attention (DSA) modules. Specifically, the DSA module fuses features from two adjacent levels through the false positive stream attention (FPSA) branch and the false negative stream attention (FNSA) branch to obtain features with diversified contextual information. We compare our method with various state-of-the-art (SOTA) lesion segmentation methods with several public datasets, including CVC-ClinicDB, Kvasir-SEG, and ISIC-2018 Task 1. The experimental results show that our method achieves SOTA performance in terms of mean Dice coefficient (mDice) and mean Intersection over Union (mIoU) with low model complexity and memory consumption.
