深度学习在胸部X线摄片筛查肺部疾病中的应用现状  

The current application status of deep learning in Chest X-ray screening for lung diseases

作　　者：李俊良 刘欣 林峙渊 龙显荣 江志航 霍颖瑜 Li Junliang;Liu Xin;Lin Zhiyuan;Long Xianrong;Jiang Zhihang;Huo Yingyu(School of Mechatronic Engineering and Automation,Foshan University,Foshan 528000,China;School of Design,Foshan University,Foshan 528000,China;Department of Imaging,the Fourth People’s Hospital of Foshan City,Foshan 528000,China;School of Computer Science and Artificial Intelligence,Foshan University,Foshan 528000,China)

机构地区：[1]佛山大学机电工程与自动化学院,佛山528000 [2]佛山大学设计学院,佛山528000 [3]佛山市第四人民医院影像科,佛山528000 [4]佛山大学计算机与人工智能学院,佛山528000

出　　处：《中国防痨杂志》2024年第12期1548-1559,共12页Chinese Journal of Antituberculosis

基　　金：广东省科技计划项目(2023A1313990095)。

摘　　要：肺部疾病种类多样、危害严重,早期发现对提高患者生存率至关重要。近年来,深度学习技术在医学影像分析中取得突破性进展,为肺部疾病的早期筛查提供了新的可能。笔者回顾了近5年的相关研究,重点探讨了卷积神经网络、Transformer模型及其混合架构在胸部X线摄片图像分析中的应用。同时,也分析了多模型集成学习策略和注意力机制在提高肺部疾病精准诊断中的潜力,旨在全面梳理和分析采用深度学习技术在胸部X线摄片筛查肺部疾病中的应用现状、面临的挑战及未来发展方向。There are various types of lung diseases with serious harm,and early detection of those diseases is crucial to improve the survival rate of patients.In recent years,deep learning technology has made a breakthrough progress in analysis of medical images,which providing new possibilities for early screening of lung diseases.The authors reviewed relevant studies in the past five years,focusing on the applications of Convolutional Neural Networks,Transformer models,and their hybrid architectures in chest X-ray(CXR)image analysis.Additionally,the potential of multi-model ensemble learning strategies and attention mechanisms in improving the diagnostic accuracy of lung diseases was also analyzed.This comprehensive review aims to systematically review and analyze the current application,challenges,and future directions of using deep learning technologies in chest X-ray screening for lung disease detection.

关 键 词：X线透视检查 神经网络(计算机) 肺疾病 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R81[自动化与计算机技术—计算机科学与技术]