We propose Uncertainty Augmented Context Attention network (UACANet) for polyp segmentation which consider a uncertain area of the saliency map. We construct a modified version of U-Net shape network with additional encoder and decoder and compute a saliency map in each bottom-up stream prediction module and propagate to the next prediction module. In each prediction module, previously predicted saliency map is utilized to compute foreground, background and uncertain area map and we aggregate the feature map with three area maps for each representation. Then we compute the relation between each representation and each pixel in the feature map. We conduct experiments on five popular polyp segmentation benchmarks, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, and achieve state-of-the-art performance. Especially, we achieve 76.6% mean Dice on ETIS dataset which is 13.8% improvement compared to the previous state-of-the-art method.

提出了Uncertainty Augmented Context Attention网络（UACANet），用于息肉分割。在该网络中，考虑到显著图的不确定区域，并构建了一个具有附加编码器和解码器的U-Net形状网络的改进版本，并在每个自下而上流的预测模块中计算显著图，并将其传播到下一个预测模块，最终实现了比以前最先进的方法更好的表现，特别是在ETIS数据集上获得了76.6％的平均Dice系数。

UACANet：用于息肉分割的不确定性增强上下文注意力网络