  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.
