文献类型：会议论文

英文题名：FCAU-Net: A Frequency Channel Attention Convolutional Neural Network for Medical Image Segmentation

作者：Tao, Chen[1]; Chen, Hongyu[1]; Wu, Ronghua[1]; Zhi, Huixiang[1]; Yan, Xiao[2]; Liu, Hongzhe[3]; Xu, Cheng[3]; Jian, Muwei[4]

第一作者：Tao, Chen

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

第一机构：Linyi University, School of Information Science and Technology, Linyi, China

通讯机构：[4]Shandong University of Finance and Economics, School of Computer 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：deep learning; Frequency Channel Attention; Medical image segmentation

摘要：Recently, U-Net and its subsequent extensions such as Attention U-Net have emerged as the leading medical image segmentation methods. However, many approaches that combine attention mechanisms with U-Net have overlooked a fundamental issue, that is, they fail to represent channel attention mechanisms using scalars. In this work, we propose a novel method called FCAU-Net, which extends the compression of channel attention mechanism to the frequency domain that combines the multi-spectral attention module with U-Net for medical image segmentation. FCAU-Net has a stronger ability to learn significant features specific to local regions than classic U-Net. In addition, we introduce a new inception block that decomposes the large kernel depth-wise convolution of the inception architecture with two parallel branches of deep convolutions, aiming to further enhance the model's feature representation and semantic information acquisition capabilities while reducing the number of parameters added by the large convolutional kernel. Experiments on multiple medical image segmentation datasets demonstrate that our method achieves better segmentation performance. ? 2023 IEEE.