生物医学工程研究2024，Vol.43Issue(3)：223-231,9.DOI:10.19529/j.cnki.1672-6278.2024.03.07
基于Goddard评分法的肺气肿自监督分级算法研究
Study on self-supervised emphysema grading algorithm based on goddard scoring method
摘要
Abstract
Aiming at the intelligent diagnosis of emphysema highly depending on high-quality annotation data,complex image spa-tial information and insufficient feature extraction,we designed an emphysema classification algorithm based on Goddard scoring meth-od.Firstly,the algorithm utilized the SimSiam framework for self-supervised learning to address the dependency on a large volume of high-quality annotated data.Then,the continuous 3D convolution module and the efficient multi-scale attention(EMA)module were introduced,to capture the key spatial information of lung images by integrating the information of upper,middle and lower lung lobes,to improve the feature extraction ability and recognition accuracy of the model were processing complex lung CT images.The experimen-tal results showed that in the grading task of the emphysema presence,mild and no emphysema,and the severity of emphysema,the accuracy of the model was 88.79%,83.44%,and 57.4%,respectively.The result indicates that this algorithm performs well in the em-physema recognition and classification,and has certain clinical significance.关键词
慢性阻塞性肺疾病/肺气肿/CT影像/自监督学习/EMA/3D卷积Key words
Chronic obstructive pulmonary disease/Emphysema/CT imaging/Self-supervised learning/EMA/3D convolution分类
医药卫生引用本文复制引用
韩云龙,王苹苹,卢绪香,杨毅,丁鹏,魏本征..基于Goddard评分法的肺气肿自监督分级算法研究[J].生物医学工程研究,2024,43(3):223-231,9.基金项目
山东省自然科学基金资助项目(No.ZR2020KF013,ZR2019ZD04,ZR2023QF094) （No.ZR2020KF013,ZR2019ZD04,ZR2023QF094）
青岛市科技惠民示范专项项目(No.23-2-8-smjk-2-nsh) （No.23-2-8-smjk-2-nsh）
山东省中医药科技项目(Q-2023070). （Q-2023070）
