[1]堵红群,李岳阳,崔方正,等.基于多维度融合的肺结节分类算法[J].中国医学物理学杂志,2024,41(11):1428-1436.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.016]
　DU Hongqun,LI Yueyang,CUI Fangzheng,et al.Lung nodule classification algorithm based on multi-dimensional fusion[J].Chinese Journal of Medical Physics,2024,41(11):1428-1436.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.016]
点击复制

基于多维度融合的肺结节分类算法()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第11期
页码:
1428-1436
栏目:
医学人工智能
出版日期:
2024-11-26

文章信息/Info

Title:
Lung nodule classification algorithm based on multi-dimensional fusion
文章编号:
1005-202X（2024）11-1428-09
作者:
堵红群1; 李岳阳2; 崔方正2; 罗海驰3; 顾中轩2
1.江南大学附属医院影像科， 江苏 无锡 214122； 2.江南大学人工智能与计算机学院， 江苏 无锡 214122； 3.江南大学物联网工程学院， 江苏 无锡 214122
Author(s):
DU Hongqun1;  LI Yueyang2;  CUI Fangzheng2;  LUO Haichi3;  GU Zhongxuan2
1.Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi 214122, China 2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China 3. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
关键词:
肺结节; 计算辅助诊断; 多尺度特征融合; 软激活映射; 平衡均方差损失
Keywords:
Keywords: lung nodule computer-aided diagnosis multi-scale feature fusion soft activation mapping; balanced mean square error loss
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2024.11.016
文献标志码:
A
摘要:
采用多维度模型融合的方法，提出一种肺结节分类算法。在肺结节假阳性减少算法基础上进行优化，在多尺度特征融合模块得到特征之后引入高层特征增强软激活映射模块，以增强模型的分类能力；针对实际分类过程中各类结节数据不平衡的问题，引入平衡均方差损失来改进模型的训练效果；采用三维和二维模型融合方式进一步提升模型分类性能。在Private Lung数据集上进行的实验证明本研究提出的模型分类准确度达到93.8%，优于现有方法。 【关键词】肺结节；计算辅助诊断；多尺度特征融合；软激活映射；平衡均方差损失
Abstract:
Abstract: A novel algorithm based on multi-dimensional fusion is proposed for classifying lung nodules. Based on the algorithm for reducing false positives of pulmonary nodules, the optimization is carried out by introducing a high-level feature enhancement soft activation mapping module after obtaining features by the multi-scale feature fusion module to improve the classification ability. To address the imbalance of different nodule data in the actual classification, a balanced mean square error loss is adopted to improve the training effect of the model. A three-dimensional and two-dimensional model fusion method is used to further improve the classification performance. The experiment conducted on a Private Lung dataset proves that the proposed model has a classification accuracy of 93.8%, outperforming the existing methods.

相似文献/References:

[1]唐思源,刘燕茹,杨敏,等.基于CT图像的肺结节检测与识别[J].中国医学物理学杂志,2019,36(7):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
　TANG Siyuan,LIU Yanru,YANG Min,et al.Detection and recognition of pulmonary nodules based on CT images[J].Chinese Journal of Medical Physics,2019,36(11):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
[2]张倩雯,陈明,秦玉芳,等.基于3D ResUnet网络的肺结节分割[J].中国医学物理学杂志,2019,36(11):1356.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]
　ZHANG Qianwen,CHEN Ming,QIN Yufang,et al.Lung nodule segmentation based on 3D ResUnet network[J].Chinese Journal of Medical Physics,2019,36(11):1356.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]
[3]曹宇,邢素霞,逄键梁,等.基于改进的VGG-16卷积神经网络的肺结节检测[J].中国医学物理学杂志,2020,37(7):940.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
　CAO Yu,XING Suxia,PANG Jianliang,et al.Detection of pulmonary nodules based on improved VGG-16 convolution neural network[J].Chinese Journal of Medical Physics,2020,37(11):940.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
[4]王乾梁,石宏理.基于改进YOLO V3的肺结节检测方法[J].中国医学物理学杂志,2021,38(9):1179.[doi:10.3969/j.issn.1005-202X.2021.09.024]
　WANG Qianliang,SHI Hongli,et al.Pulmonary nodule detection based on improved YOLO V3[J].Chinese Journal of Medical Physics,2021,38(11):1179.[doi:10.3969/j.issn.1005-202X.2021.09.024]
[5]刘雲,王一达,张成秀,等.基于深度学习结合解剖学注意力机制的肺结节良恶性分类[J].中国医学物理学杂志,2022,39(11):1441.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.019]
　LIU Yun,WANG Yida,ZHANG Chengxiu,et al.Classification of benign and malignant pulmonary nodules by deep learning with anatomy-based attention mechanism[J].Chinese Journal of Medical Physics,2022,39(11):1441.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.019]
[6]王新宇,赵静文,刘翔,等.人工智能在肺结节筛查和肺癌诊断中的应用[J].中国医学物理学杂志,2023,40(9):1182.[doi:DOI:10.3969/j.issn.1005-202X.2023.09.020]
　WANG Xinyu,ZHAO Jingwen,LIU Xiang,et al.Applications of artificial intelligence in lung nodule detection and lung cancer diagnosis[J].Chinese Journal of Medical Physics,2023,40(11):1182.[doi:DOI:10.3969/j.issn.1005-202X.2023.09.020]
[7]温帆,杨萍,张鑫,等.基于特征增强的多分支U-Net肺结节分割[J].中国医学物理学杂志,2023,40(11):1343.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.005]
　WEN Fan,YANG Ping,ZHANG Xin,et al.Pulmonary nodule segmentation using multi-branch U-Net based on feature enhancement[J].Chinese Journal of Medical Physics,2023,40(11):1343.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.005]
[8]刘涌涛,王宝珠,郭志涛.基于改进YOLOv7网络模型的肺结节检测算法[J].中国医学物理学杂志,2023,40(12):1509.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.009]
　LIU Yongtao,WANG Baozhu,GUO Zhitao.Lung nodule detection algorithm using improved YOLOv7 network model[J].Chinese Journal of Medical Physics,2023,40(11):1509.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.009]
[9]牛树国,周福兴,颜克松,等.不同CT阈值下实性成分占比对小肺癌浸润性预测的影响[J].中国医学物理学杂志,2024,41(3):323.[doi:DOI:10.3969/j.issn.1005-202X.2024.03.009]
　NIU Shuguo,ZHOU Fuxing,YAN Kesong,et al.Predictive value of consolidation/tumor ratio at different CT thresholds for invasiveness in small lung cancer[J].Chinese Journal of Medical Physics,2024,41(11):323.[doi:DOI:10.3969/j.issn.1005-202X.2024.03.009]
[10]丛玉林,徐小虎,沈春林,等.基于CT特征构建预测肺结节良恶性的机器学习模型[J].中国医学物理学杂志,2024,41(10):1315.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.017]
　CONG Yulin,XU Xiaohu,SHEN Chunlin,et al.Machine learning model predicts benign and malignant pulmonary nodules based on CT features[J].Chinese Journal of Medical Physics,2024,41(11):1315.[doi:DOI:10.3969/j.issn.1005-202X.2024.10.017]

备注/Memo

备注/Memo:
【收稿日期】2024-06-13 【基金项目】国家自然科学基金（U1836218） 【作者简介】堵红群，硕士，主任医师，研究方向：医学图像处理、人工智能在医疗影像中的应用，E-mail: 15301516381@163.com 【通信作者】李岳阳，博士，副教授，研究方向：医学图像处理、人工智能在医疗影像中的应用，E-mail: lyueyang@jiangnan.edu.cn
更新日期/Last Update: 2024-11-26