To address the challenges of artifacts,blurred boundaries,and low accuracy in the seg-mentation results of polyp images,a polyp segmentation network based on hybrid reverse attention mechanism is proposed.The network incorporates a U-shaped structure and introduces a multi-scale parallel dilated convolution attention module.This module helps preserve finer-grained multi-scale features during down sampling,reducing the loss of important details.Additionally,dense con-nectivity and feature fusion are employed to cross stage partial module to bridge semantic differ-ences between contexts and enhance detailed features.Furthermore,a combination of positional at-tention and channel attention,integrated with the inverse attention strategy,is employed to learn lo-cation and channel features while establishing the region-boundary relationship for accurate polyp and normal mucosa segmentation.Experimental results demonstrate that the polyp segmentation network based on hybrid reverse attention mechanism improves the segmentation accuracy,reduces artifacts outside the boundary,and mitigates the boundary blurring issues to a certain extent.
NETL
NSTL
维普
万方数据
