摘要
乳腺癌组织病理学检查是乳腺癌诊断的“金标准”。乳腺癌组织病理学图像的分类已经成为医学图像处理领域的研究热点。图像的精确分类,在辅助医生诊断病情、满足临床应用需求等方面有着重大的应用价值。文中跟踪了乳腺癌组织病理学图像分类算法的研究进展,分析了相关算法的优缺点。按照是否需要手动提取图像特征,将乳腺癌组织病理学图像分类算法分为两大类,分别是传统的人工提取乳腺癌组织病理学图像特征的分类方法,以及基于深度学习算法的乳腺癌组织病理学图像分类方法。然后,对基于深度学习算法的乳腺癌组织病理学图像进行二分类或多分类的研究进行了进一步跟踪。最后,给出了应用深度学习最新理论的乳腺癌组织病理学图像分类算法,得出乳腺癌组织病理学图像分类研究的结论,并讨论了进一步的研究方向。
Histopathological examination of breast cancer is the“gold standard”for breast cancer diagnosis.The classification of breast cancer histopathological images has become a hot research topic in the field of medical image processing.The accurate classification of images has great application value in the fields of assisting doctors to diagnose the disease and meeting the needs of clinical application.This paper assesses the advantages and disadvantages of one breast cancer histopathological image classification algorithm.The methods are classified into two categories,depending on whether or not it is necessary to manually extract feature of breast cancer histopathological images or if the classification of breast cancer histopathological images can be based on a deep learning algorithm.The research on binary or multi-classification of breast cancer histopathology images is further tracked.Finally,the classification algorithm of breast cancer histopathology images using the latest theory of deep learning is gi-ven.Conclusions of the classification study of breast cancer histopathological images are drawn,and possible directions in the future are discussed.
作者
满芮
杨萍
季程雨
许博文
MAN Rui;YANG Ping;JI Cheng-yu;XU Bo-wen(Smart City College,Beijing Union University,Beijing 100101,China;Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《计算机科学》
CSCD
北大核心
2020年第S02期145-150,共6页
Computer Science
关键词
乳腺癌
病理学图像
特征提取
图像分类
深度学习
Breast cancer
Histopathological images
Feature extraction
Image classification
Deep learning
