Breast cancer histopathological image classification using a hybrid deep neural network

doi:10.1016/j.ymeth.2019.06.014

	Breast cancer histopathological image classification using a hybrid deep neural network
	Yan, Rui 1,2; Ren, Fei 2; Wang, Zihao 2,3; Wang, Lihua 4; Zhang, Tong 4; Liu, Yudong 2; Rao, Xiaosong 4; Zheng, Chunhou 1; Zhang, Fa 2
	2020-02-15
发表期刊	METHODS
ISSN	1046-2023
卷号	173 页码:52-60
摘要	Even with the rapid advances in medical sciences, histopathological diagnosis is still considered the gold standard in diagnosing cancer. However, the complexity of histopathological images and the dramatic increase in workload make this task time consuming, and the results may be subject to pathologist subjectivity. Therefore, the development of automatic and precise histopathological image analysis methods is essential for the field. In this paper, we propose a new hybrid convolutional and recurrent deep neural network for breast cancer histopathological image classification. Based on the richer multilevel feature representation of the histopathological image patches, our method integrates the advantages of convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches are preserved. The experimental results show that our method outperforms the state-of-the-art method with an obtained average accuracy of 91.3% for the 4-class classification task. We also release a dataset with 3771 breast cancer histopathological images to the scientific community that is now publicly available at http://ear.ict.ac.cn/?page_id =1616. Our dataset is not only the largest publicly released dataset for breast cancer histopathological image classification, but it covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low classification accuracy of benign images.
关键词	Breast cancer Histopathological images Image classification Deep neural network Dataset
DOI	10.1016/j.ymeth.2019.06.014
收录类别	SCI
语种	英语
资助项目	Peking University International Hospital Research Grant[YN2018ZD05] ; National Key Research and Development Program of China[2017YFE0103900] ; National Key Research and Development Program of China[2017YFA0504702] ; NSFC[U1611263] ; NSFC[U1611261] ; NSFC[61502455] ; NSFC[61672493] ; Beijing Municipal Natural Science Foundation[L182053] ; Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase)
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemical Research Methods ; Biochemistry & Molecular Biology
WOS记录号	WOS:000520948800007
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计	被引频次：185[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14026
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Rao, Xiaosong; Zheng, Chunhou; Zhang, Fa
作者单位	1.Anhui Univ, Coll Comp Sci & Technol, Hefei, Peoples R China 2.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Peking Univ, Dept Pathol, Int Hosp, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yan, Rui,Ren, Fei,Wang, Zihao,et al. Breast cancer histopathological image classification using a hybrid deep neural network[J]. METHODS,2020,173:52-60.
APA	Yan, Rui.,Ren, Fei.,Wang, Zihao.,Wang, Lihua.,Zhang, Tong.,...&Zhang, Fa.(2020).Breast cancer histopathological image classification using a hybrid deep neural network.METHODS,173,52-60.
MLA	Yan, Rui,et al."Breast cancer histopathological image classification using a hybrid deep neural network".METHODS 173(2020):52-60.