HarDNet-CPS: Colorectal polyp segmentation based on Harmonic Densely United Network

Abstract

Colorectal cancer (CRC) is one of the top three cancers in the world. The disease is initially caused by the deterioration of polyps that are regarded as the predecessor of CRC. Many people die from the disease yearly, so clinical specialists pay high attention to the polyp. It is significant to improve the accurate recognition of polyp areas for clinicians to find the lesion area. Although a set of existing methods have been used in segmentation of polyps, the segmentation results still can be improved further. This paper proposed a new model called Harmonic Densely United Network for Colorectal Polyp Segmentation (HarDNet-CPS). Harmonic Densely Connected Network-68 is used as the backbone, in which branches with multi-scale channel attention (MS-CAM) are cleverly embedded to extract features from channel attention; the pyramid feature fusion technique is used to adaptively spatial feature fusion (ASFF) in the decoding area, and then output is obtained through aggregation module. The mean Dice in Kvasir and CVC-ClinicDB data sets both have broken through 0.9 for the first time. HarDNet-CPS has advantages such as good robustness and high accuracy, revealing the effectiveness of automated polyp detection. It will bring great help to clinical diagnosis and treatment.