PDNet: an advanced architecture for polyp image segmentation

Abstract

In order to improve the segmentation accuracy of polyp image segmentation under colonoscopy, we propose PVT Dual-Upsampling Net (PDNet). PDNet adopts the encoder network based on Transformer as the backbone network for downsampling, and designs a dual upsampling module based on cascaded fusion network and simple connection network to recover the loss of high-level image features caused by the downsampling process, and obtains a high-level semantic feature map with the same resolution as the input image. The multi-feature fusion module is used to aggregate the low-level feature map and high-level semantic feature map. We validate the model on three publicly available datasets, and our experimental evaluations show that the suggested architecture produces good segmentation results on datasets.