Medical image segmentation method based on multi-feature interaction and fusion over cloud computing

作者全名："He, Xianyu; Qi, Guanqiu; Zhu, Zhiqin; Li, Yuanyuan; Cong, Baisen; Bai, Litao"

作者地址："[He, Xianyu; Zhu, Zhiqin; Li, Yuanyuan] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China; [He, Xianyu; Bai, Litao] Chongqing Med Univ, Affiliated Hosp 2, Dept Integrated Chinese & Western Med, Chongqing 400010, Peoples R China; [Qi, Guanqiu] State Univ New York Buffalo State, Comp Informat Syst Dept, Buffalo, NY 14222 USA; [Cong, Baisen] DH Shanghai Diagnost Co Ltd, Diagnost Digital, Shanghai 200335, Peoples R China; [Cong, Baisen] Danaher Co, Shanghai 200335, Peoples R China"

通信作者："Bai, LT (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Integrated Chinese & Western Med, Chongqing 400010, Peoples R China."

来源：SIMULATION MODELLING PRACTICE AND THEORY

ESI学科分类：COMPUTER SCIENCE

WOS号：WOS:000990513500001

JCR分区：Q1

影响因子：3.5

年份：2023

卷号：126

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Medical image segmentation; Transformer; Cloud computing; Interactive fusion

摘要："Medical image segmentation is a crucial task in computer-aided diagnosis. While deep learning has significantly improved this field, relying solely on local computing power makes it challenging to achieve real-time segmentation results. Furthermore, traditional convolutional neural networks (CNNs) lack the ability to extract global features. To address these issues, this paper proposes a cloud-based medical image segmentation method that leverages multi -feature extraction and interactive fusion. Specifically, this method employs cloud computing to process a large number of medical images and overcome local computing power limitations. It also combines Transformer and CNNs to extract global and local features, respectively, and introduces an interactive fusion attention module to improve segmentation accuracy. The proposed approach is validated on multiple medical image datasets, and experimental results demonstrate its effectiveness and progress."

基金机构："National Natural Science Foundation of China [82205049, 62276037]; Natural Science Foundation of Chongqing, China [cstc2020jcyj-msxmX0259]; Chongqing medical scientific research project, China (Joint project of Chongqing Health Commission and Science and Technology Bureau) [2022MSXM184]; Kuanren Talents Program of the Second affiliated Hospital of Chongqing Medical University, China; Special key project of Chongqing technology innovation and application development, China [CSTB2022TIAD-KPX0039]; Basic Research and Frontier Exploration Project of Yuzhong District, Chongqing, China [20210164]"

基金资助正文："This research is jointed sponsored by National Natural Science Foundation of China (82205049, 62276037) , Natural Science Foundation of Chongqing, China (cstc2020jcyj-msxmX0259) , Chongqing medical scientific research project, China (Joint project of Chongqing Health Commission and Science and Technology Bureau, 2022MSXM184) and Kuanren Talents Program of the Second affiliated Hospital of Chongqing Medical University, China, Special key project of Chongqing technology innovation and application development, China: CSTB2022TIAD-KPX0039, Basic Research and Frontier Exploration Project of Yuzhong District, Chongqing, China, Grant/Award Number: 20210164."