{"type":"ScholarlyArticle","title":"Sub-Band Based Attention for Robust Polyp Segmentation","authors":[{"name":"Xianyong Fang","slug":"xianyong-fang","given":"Xianyong","family":"Fang","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Yuqing Shi","slug":"yuqing-shi","given":"Yuqing","family":"Shi","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Qingqing Guo","slug":"qingqing-guo","given":"Qingqing","family":"Guo","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Linbo Wang","slug":"linbo-wang","given":"Linbo","family":"Wang","affiliation":"School of Computer Science and Technology , Anhui University"},{"name":"Zhengyi Liu","slug":"zhengyi-liu","given":"Zhengyi","family":"Liu","affiliation":"School of Computer Science and Technology , Anhui University"}],"abstract":"This article proposes a novel spectral domain based solution to the challenging polyp segmentation. The main contribution is based on an interesting finding of the significant existence of the middle frequency sub-band during the CNN process. Consequently, a Sub-Band based Attention (SBA) module is proposed, which uniformly adopts either the high or middle sub-bands of the encoder features to boost the decoder features and thus concretely improve the feature discrimination. A strong encoder supplying informative sub-bands is also very important, while we highly value the local-and-global information enriched CNN features. Therefore, a Transformer Attended Convolution (TAC) module as the main encoder block is introduced. It takes the Transformer features to boost the CNN features with stronger long-range object contexts. The combination of SBA and TAC leads to a novel polyp segmentation framework, SBA-Net. It adopts TAC to effectively obtain encoded features which also input to SBA, so that efficient sub-bands based attention maps can be generated for progressively decoding the bottleneck features. Consequently, SBA-Net can achieve the robust polyp segmentation, as the experimental results demonstrate.","doi":"10.24963/ijcai.2023/82","url":"https://ijcai.org/proceedings/2023/82","pdf":"https://ijcai.org/proceedings/2023/0082.pdf","markdown":"https://ijcai.org/proceedings/2023/82.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/82","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":82,"pages":{"first":736,"last":744},"keywords":["Data Mining: DM: Frequent pattern mining","Multidisciplinary Topics and Applications: MDA: Health and medicine","Machine Learning: ML: Applications","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Ahmed and Ali, 2020] Awadelrahman Ahmed and MA Ali. 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