Edge-aware Feature Aggregation Network for Polyp Segmentation  

作　　者：Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan 

机构地区：[1]PCA Lab,Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education,School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,210094,China [2]School of Computer Science and Engineering,Northwestern Polytechnical University(NPU),Xi’an,710129,China [3]School of Computer Science and Engineering,Southeast University,Nanjing,211189,China [4]Computer Vision Lab,ETH Zürich,Zürich,8092,Switzerland

出　　处：《Machine Intelligence Research》2025年第1期101-116,共16页机器智能研究(英文版)

基　　金：supported in part by National Natural Science Foundation of China(Nos.62172228,62201263,62106043 and 62201265).

摘　　要：Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical practice.However,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes.In this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation.Specifically,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer strategy.Besides,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale variation.Further,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual information.Finally,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation maps.Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Our implementation code and segmentation maps will be publicly at https://github.com/taozh2017/EFANet.

关 键 词：Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module 

分 类 号：R318[医药卫生—生物医学工程] TP391.41[医药卫生—基础医学]