肺腺癌亚型分类技术研究进展  

Advances in Classification of Lung Adenocarcinoma Subtypes

作　　者：刘茗传 张魁星[1] 江梅[1] 张晓丽[1] 李丽萍 LIU Mingchuan;ZHANG Kuixing;JIANG Mei;ZHANG Xiaoi;LI Liping(School of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355

出　　处：《计算机工程与应用》2023年第17期67-79,共13页Computer Engineering and Applications

基　　金：国家自然科学基金(61872225);山东省研究生教育优质课程建设项目(SDYKC19148)。

摘　　要：肺腺癌存在多种不同类型,各有表征,准确对其分类是临床诊断和治疗的重要依据。从肺腺癌组织病理学、影像学、基因组学等多个方面进行肺腺癌亚型分类研究一直是临床研究的热点问题之一。特别是近年来机器学习和深度学习技术的发展为肺腺癌分类研究提供了新的方法和思路。详细阐述了当前肺腺癌分类技术的研究进展,对各种亚型分类技术应用进展进行了系统的评价。总结了各类分类技术的优缺点、传统分类方法的难易程度和常用的机器学习、深度学习技术模型的算法复杂度,分析了当前研究的相关问题,并对未来的研究方向进行了展望。There are many different types of lung adenocarcinoma,and each has its own characteristics.Accurate classifi-cation of lung adenocarcinoma is an important basis for clinical diagnosis and treatment.Classification of lung adenocarci-noma subtypes from histopathology,imaging,genomics and other aspects has always been one of the hot issues in clinical research.Especially in recent years,the development of machine learning and deep learning technology has provided new methods and ideas for the classification of lung adenocarcinoma.In this paper,the research progress of the classification techniques of lung adenocarcinoma is described in detail,and the application progress of the classification techniques of various subtypes is systematically evaluated.This paper summarizes the advantages and disadvantages of various classification technologies,the difficulty of traditional classification methods and the algorithm complexity of common machine learning and deep learning technology models,analyzes the relevant problems in current research,and looks forward to the future research direction.

关 键 词：肺腺癌 分类技术 计算机断层扫描(CT)影像 病理组织图像 机器学习 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]