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Divide-and-Attention Network for HE-Stained Pathological Image Classification

文献类型：期刊论文

作者Yan, Rui2,3; Yang, Zhidong3; Li, Jintao3; Zheng, Chunhou1; Zhang, Fa3
刊名BIOLOGY-BASEL
出版日期2022-07-01
卷号11期号:7页码:17
关键词pathological image classification attention mechanism convolutional neural network knowledge embedding
DOI10.3390/biology11070982
英文摘要Simple Summary We propose a Divide-and-Attention network that can learn representative pathological image features with respect to different tissue structures and adaptively focus on the most important ones. In addition, we introduce deep canonical correlation analysis constraints in the feature fusion process of different branches, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. Extensive experiments on three different pathological image datasets show that the proposed method achieved competitive results. Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin (HE)-stained pathological image classification. The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. In this way, the DANet can learn representative features with respect to different tissue structures and adaptively focus on the most important ones, thereby improving classification performance. In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. The DCCA constraints play the role of branch fusion attention, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. The experimental results of three datasets demonstrate the superiority of the DANet, with an average classification accuracy of 92.5% on breast cancer classification, 95.33% on colorectal cancer grading, and 91.6% on breast cancer grading tasks.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA16021400] ; National Key Research and Development Program of China[2021YFF0704300] ; NSFC[61932018] ; NSFC[62072441] ; NSFC[62072280]
WOS研究方向Life Sciences & Biomedicine - Other Topics
语种英语
WOS记录号WOS:000831586600001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/19484]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zheng, Chunhou; Zhang, Fa
作者单位1.Anhui Univ, Sch Artificial Intelligence, Hefei 230093, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100045, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Rui,Yang, Zhidong,Li, Jintao,et al. Divide-and-Attention Network for HE-Stained Pathological Image Classification[J]. BIOLOGY-BASEL,2022,11(7):17.
APA Yan, Rui,Yang, Zhidong,Li, Jintao,Zheng, Chunhou,&Zhang, Fa.(2022).Divide-and-Attention Network for HE-Stained Pathological Image Classification.BIOLOGY-BASEL,11(7),17.
MLA Yan, Rui,et al."Divide-and-Attention Network for HE-Stained Pathological Image Classification".BIOLOGY-BASEL 11.7(2022):17.

入库方式： OAI收割

来源：计算技术研究所

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