LACINet: A Lesion-Aware Contextual Interaction Network for Polyp Segmentation

Automatic polyp segmentation is critical for early prevention and diagnosis of colorectal cancer (CRC). However, diverse foreground appearance and complicated background interference severely degrade the performance of pixel-level prediction. The excessive computational overheads further hinder the practical clinical applications of the existing methods. In this article, we propose a novel lesion-aware contextual interaction network (LACINet), which aims to explore the long-range dependencies and global contexts with friendly computing resource consumption for polyp segmentation. Specifically, we present a lesion-aware pyramid mechanism (LPM) to weaken the influence of background noise and refine lesion-related features. We also develop a robust representation enhancement decoder (RED) to learn global feature representations and aggregate the multilevel contexts. In RED, we first build a nonlocal contextual lesion interaction module (NCLIM) to integrate the cross-level contextual information for obtaining the intrinsic feature representations, and then design a tri-branching multiscale perceptual self-attention module (TMPSM) to sufficiently excavate the global features. Notably, we introduce an asymmetric multibranch strategy to alleviate the computational burden. The experimental results on several widely used benchmark datasets demonstrate the superior performance of our proposed LACINet in comparison with state-of-the-art methods.