计算机应用研究2024，Vol.41Issue(11)：3502-3508,7.DOI:10.19734/j.issn.1001-3695.2023.11.0634
基于自分块轻量化Transformer的医学图像分割网络
Medical image segmentation network based on self-partitioning lightweight Transformer
摘要
Abstract
The traditional medical image segmentation network has a large number of parameters and slow computing speed,and cannot applies effectively to the real-time detection technology.To address this issue,this paper proposed a lightweight medical image segmentation network called SPTFormer.Firstly,this network constructed a self-blocking Transformer module,which reshaped the feature map through an adaptive blocking strategy and utilized parallel computing to improve the attention operation speed while paying attention to local detail features.Secondly,this network constructed an SR-CNN module,which used the shift-restored operation to improve the ability to capture local spatial information.By experimenting on ISIC 2018,BUSI,CVC-ClinicDB and 2018 data science bowl,compared with the TransUNet model based on Transformer,the accuracy of the proposed network improves by 4.28％,3.74％,6.50％,and 1.16％,respectively,the GPU computation time reduces by 58％.The proposed network has better performance in medical image segmentation applications,which can well balance the network accuracy and complexity,and provides a new solution for real-time computer-aided diagnosis.关键词
医学图像分割/轻量化网络/TransformerKey words
medical image segmentation/lightweight network/Transformer分类
信息技术与安全科学引用本文复制引用
张文杰,宋艳涛,王克琪,张越..基于自分块轻量化Transformer的医学图像分割网络[J].计算机应用研究,2024,41(11):3502-3508,7.基金项目
山西省回国留学人员科研教研资助项目(2023-015) （2023-015）
国家自然科学基金资助项目(61906114) （61906114）
