Polyp Segmentation Based on Involution and Coordinate Reverse Attention

doi:10.3969/j.issn.1006-2475.2024.11.013

Abstract

Abstract: Polyps in colon images are characterized by variable morphology and blurred edges. Aiming at the problems of the current neural networks for polyp segmentation， such as the inadequate feature extraction due to the inherent limitations of convolution， and the unsatisfactory segmentation due to the incomplete relationship between area and boundary， a network（IN-CRNet） based on Involution and coordinate reverse attention was proposed. In the encoder， an Involution-based Receptive Field Module（InRFB） was designed to adaptively capture contextual information at different scales. It improved the ability to detect complex and variable polyps. In the decoder， a coordinate reverse attention module（CRA） was designed to focus on the importance of both regions and edges and establish the relationship between them. It gradually refined the details of the edges from the bottom to up. The experimental results on five public datasets show that IN-CRNet effectively improves the accuracy of segmentation and has good generalization ability.

Key words: , image segmentation, colorectal polyps, Involution, coordinate reverse attention

CLC Number:

TP391

WAN Hongwei, CHEN Pinghua. Polyp Segmentation Based on Involution and Coordinate Reverse Attention [J]. Computer and Modernization, 2024, 0(11): 84-90.

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