文献类型：会议论文

英文题名：Parallel Adaptive Network for Polyp Segmentation

作者：Yu, Xiaoyang[1]; Zhong, Yanjie[1]; Yan, Xiao[2]; Jian, Muwei[3]; Liu, Hongzhe[4]; Xu, Cheng[4]

第一作者：Yu, Xiaoyang

机构：[1] Shandong University of Finance and Economics, College of Computer Science and Technology, Jinan, China; [2] The First Affiliated Hospital of Ningbo University, Ningbo Clinical Research Center for Hematologic Malignancie, Department of Haematology, Ningbo, China; [3] Shandong University of Finance and Economics, School of Information Science and Technology, Jinan, China; [4] Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, China

第一机构：Shandong University of Finance and Economics, College of Computer Science and Technology, Jinan, China

通讯机构：[3]Shandong University of Finance and Economics, School of Information Science and Technology, Jinan, China

会议论文集：Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

会议日期：August 28, 2023 - August 31, 2023

会议地点：Portsmouth, United kingdom

语种：英文

外文关键词：attention mechanism; colonoscopy; dynamic convolution; polyp segmentation

摘要：This paper proposes a parallel adaptive network named PA-Net for polyp segmentation, which enables to dynamically adjust convolution kernels and attention weights according to the input. It enhances the focus on key regions, thereby ameliorating the challenge of hard identification due to the complex morphology and the blurring of the border between polyp and mucosa. Furthermore, as a lightweight network, PA-Net does not lead to excessive computational overhead and real-time efficiency of 34 FPS achieved. The experimental results show that PA-Net achieves excellent segmentation performance on public colonoscopy dataset. ? 2023 IEEE.