HRENet: A Hard Region Enhancement Network for Polyp Segmentation

Automatic polyp segmentation in the screening system is of great practical significance for the diagnosis and treatment of colorectal cancer. However, accurate segmentation in the colonoscopy images still remains a challenge. In this paper, we propose a hard region enhancement network (HRENet) based on an encoder-decoder framework. Specifically, we design an informative context enhancement (ICE) module to explore and intensify the features from the lower-level encoder with explicit attention on hard regions. We also develop an adaptive feature aggregation (AFA) module to select and aggregate the features from multiple semantic levels. In addition, we train the model with a proposed edge and structure consistency aware loss (ESCLoss) to further boost the performance. Extensive experiments on three public datasets show that our proposed algorithm outperforms the state-of-the-art approaches in terms of both learning ability and generalization capability. In particular, our HRENet achieves a mIoU of 92.11% and a Dice of 92.56% on Kvasir-SEG dataset. And the model trained with Kvasir-SEG and CVC-Clinic DB retains a high inference performance on the unseen dataset CVC-Colon DB with a mIoU of 88.42% and a Dice of 85.26%. The code is available at: https://github.com/CathySH/HRENet.