CATNet: A Cross Attention and Texture-Aware Network for Polyp Segmentation

Abstract

Polyp segmentation is a challenging task, as some polyps exhibit similar textures to surrounding tissues, making them difficult to distinguish. Therefore, we present a parallel cross-attention and texture-aware network to address this challenging task. CATNet incorporates the parallel cross-attention mechanism, Residual Feature Fusion Module, and texture-aware module. Initially, polyp images undergo processing in our backbone network to extract multi-level polyp features. Subsequently, the parallel cross-attention mechanism sequentially captures channel and spatial dependencies across multi-scale polyp features, thereby yielding enhanced representations. These enhanced representations are then input into multiple texture-aware modules, which facilitate polyp segmentation by accentuating subtle textural disparities between polyps and the background. Finally, the Residual Feature Fusion module integrates the segmentation results with the previous layer of enhanced representations. This process serves to eliminate background noise and enhance intricate details. We assess the efficacy of our proposed method across five distinct polyp datasets. On three unseen datasets, CVC-300, CVC-ColonDB, and ETIS. We achieve mDice scores of 0.916, 0.817, and 0.777, respectively. Experimental results unequivocally demonstrate the superior performance of our approach over current models. The proposed CATNet addresses the challenges posed by textural similarities, setting a benchmark for future advancements in automated polyp detection and segmentation.