计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210800232-9.doi: 10.11896/jsjkx.210800232

• 图像处理&多媒体技术 • 上一篇    下一篇

基于卷积神经网络的乳腺癌组织病理学图像分类研究综述

张喜科, 马志庆, 赵文华, 崔冬梅   

  1. 山东中医药大学智能与信息工程学院 济南 250355
  • 出版日期:2022-11-10 发布日期:2022-11-21
  • 通讯作者: 马志庆(mazhq126@163.com)
  • 作者简介:(zhangxk1024@163.com)
  • 基金资助:
    山东省研究生教育质量提升计划课题(SDYJG19143);山东中医药大学2019年教育教学研究重点课题(ZYZ2019009);山东中医药大学研究阐释党的十九届四中全会精神专项课题(SZQH202009)

Review on Classification of Breast Cancer Histopathological Images Based on Convolutional Neural Network

ZHANG Xi-ke, MA Zhi-qing, ZHAO Wen-hua, CUI Dong-mei   

  1. College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:ZHANG Xi-ke,born in 1997,postgra-duate.His main research interests include medical image processing and analysis.
    MA Zhi-qing,born in 1964,professor,master supervisor.His main research interests include medical image proces-sing and analysis.
  • Supported by:
    Project of Postgraduate Education Quality Improvement Plan in Shandong Province(SDYJG19143),Key Project of Education and Teaching Research in Shandong University of Traditional Chinese Medicine in 2019(ZYZ2019009) and Special Project of Studying and Explaining the Spirit of The Fourth Plenary Session of the 19th CPC Central Committee in Shandong University of Traditional Chinese Medicine(SZQH202009).

摘要: 乳腺癌组织病理学检查是确诊乳腺癌的“金标准”。基于卷积神经网络的乳腺癌组织病理学图像分类已经成为医学图像处理与分析领域的研究热点之一。自动且精确的乳腺癌组织病理学图像分类在临床上具有重要的应用价值。首先介绍了两个目前广泛应用于乳腺癌组织病理学图像分类的公开数据集及各自的评价标准。然后,重点阐述了卷积神经网络在两个数据集上的研究进展。在描述研究进展的过程中,分析了部分模型准确率较低的原因,并对提升模型性能给出了一些建议。最后,讨论了乳腺癌组织病理学图像分类目前存在的问题及对未来的展望。

关键词: 乳腺癌, 组织病理学图像, 图像分类, 卷积神经网络

Abstract: Histopathological examination of breast cancer is the “gold standard” for the diagnosis of breast cancer.The classification of breast cancer histopathological images based on convolutional neural network has become one of the research hotspots in the field of medical image processing and analysis.Automatic and accurate classification of breast cancer histopathological images has important clinical application value.Firstly,two public datasets widely used in the classification of breast cancer histopathological images and their evaluation criteria are introduced.Then,the research progress of convolutional neural network on two datasets is mainly elaborated.In the process of describing the research progress,the reasons for the low accuracy of some models are analyzed,and some suggestions are given to improve the performance of the models.Finally,existing problems and future prospects of breast cancer histopathological image classification are discussed.

Key words: Breast cancer, Histopathological image, Image classification, Convolutional neural networks

中图分类号: 

  • TP391
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