ATENet: Adaptive Tiny-Object Enhanced Network for Polyp Segmentation

Abstract

Polyp segmentation is of great importance for the diagnosis and treatment of colorectal cancer. However, it is difficult to segment polyps accurately due to a large number of tiny polyps and the low contrast between polyps and the surrounding mucosa. To address this issue, we design an Adaptive Tiny-object Enhanced Network (ATENet) for tiny polyp segmentation. The proposed ATENet has two advantages: First, we design an adaptive tiny-object encoder containing three parallel branches, which can effectively extract the shape and position features of tiny polyps and thus improve the segmentation accuracy of tiny polyps. Second, we design a simple enhanced feature decoder, which can not only suppress the background noise of feature maps, but also supplement the detail information to improve further the polyp segmentation accuracy. Extensive experiments on three benchmark datasets demonstrate that the proposed ATENet can achieve the state-of-the-art performance while maintaining low computational complexity.