{"type":"ScholarlyArticle","title":"ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation","authors":[{"name":"Xiuquan Du","slug":"xiuquan-du","given":"Xiuquan","family":"Du","affiliation":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education , Anhui University , Hefei , China; School of Computer Science and Technology , Anhui University , Hefei , China"},{"name":"Xuebin Xu","slug":"xuebin-xu","given":"Xuebin","family":"Xu","affiliation":"School of Computer Science and Technology , Anhui University , Hefei , China"},{"name":"Kunpeng Ma","slug":"kunpeng-ma","given":"Kunpeng","family":"Ma","affiliation":"School of Computer Science and Technology , Anhui University , Hefei , China"}],"abstract":"Precise segmentation of polyps from colonoscopic images is extremely significant for the early diagnosis and treatment of colorectal cancer. However, it is still a challenging task due to: (1)the boundary between the polyp and the background is blurred makes delineation difficult; (2)the various size and shapes causes feature representation of polyps difficult. In this paper, we propose an integration context-based reverse-contour guidance network (ICGNet) to solve these challenges. The ICGNet firstly utilizes a reverse-contour guidance module to aggregate low-level edge detail information and meanwhile constraint reverse region. Then, the newly designed adaptive context module is used to adaptively extract local-global information of the current layer and complementary information of the previous layer to get larger and denser features. Lastly, an innovative hybrid pyramid pooling fusion module fuses the multi-level features generated from the decoder in the case of considering salient features and less background. Our proposed approach is evaluated on the EndoScene, Kvasir-SEG and CVC-ColonDB datasets with eight evaluation metrics, and gives competitive results compared with other state-of-the-art methods in both learning ability and generalization capability.","doi":"10.24963/ijcai.2022/123","url":"https://ijcai.org/proceedings/2022/123","pdf":"https://ijcai.org/proceedings/2022/0123.pdf","markdown":"https://ijcai.org/proceedings/2022/123.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/123","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":123,"pages":{"first":877,"last":883},"keywords":["Computer Vision: Segmentation","Computer Vision: Biomedical Image Analysis","Multidisciplinary Topics and Applications: Health and Medicine"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986611","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984754","length_sec":346,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bernal et al., 2012] Jorge Bernal, Javier Sánchez, and Fer- nando Vilarino. 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