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Breast cancer histopathological image classification using a hybrid deep neural network

文献类型：期刊论文

作者Yan, Rui3,4; Ren, Fei4; Wang, Zihao1,4; Wang, Lihua2; Zhang, Tong2; Liu, Yudong4; Rao, Xiaosong2; Zheng, Chunhou3; Zhang, Fa4
刊名METHODS
出版日期2020-02-15
卷号173页码:52-60
关键词Breast cancer Histopathological images Image classification Deep neural network Dataset
ISSN号1046-2023
DOI10.1016/j.ymeth.2019.06.014
英文摘要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.
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
语种英语
WOS记录号WOS:000520948800007
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/14025]  
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.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Peking Univ, Dept Pathol, Int Hosp, Beijing, Peoples R China
3.Anhui Univ, Coll Comp Sci & Technol, Hefei, Peoples R China
4.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, 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.

入库方式： OAI收割

来源：计算技术研究所

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