Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images

doi:10.3390/s22114061

	Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images
	Yan, Rui 1,2; Ren, Fei 1; Li, Jintao 1; Rao, Xiaosong 3,4; Lv, Zhilong 1; Zheng, Chunhou 5; Zhang, Fa 1
	2022-06-01
发表期刊	SENSORS
卷号	22 期号:11 页码:15
摘要	Breast cancer grading methods based on hematoxylin-eosin (HE) stained pathological images can be summarized into two categories. The first category is to directly extract the pathological image features for breast cancer grading. However, unlike the coarse-grained problem of breast cancer classification, breast cancer grading is a fine-grained classification problem, so general methods cannot achieve satisfactory results. The second category is to apply the three evaluation criteria of the Nottingham Grading System (NGS) separately, and then integrate the results of the three criteria to obtain the final grading result. However, NGS is only a semiquantitative evaluation method, and there may be far more image features related to breast cancer grading. In this paper, we proposed a Nuclei-Guided Network (NGNet) for breast invasive ductal carcinoma (IDC) grading in pathological images. The proposed nuclei-guided attention module plays the role of nucleus attention, so as to learn more nuclei-related feature representations for breast IDC grading. In addition, the proposed nuclei-guided fusion module in the fusion process of different branches can further enable the network to focus on learning nuclei-related features. Overall, under the guidance of nuclei-related features, the entire NGNet can learn more fine-grained features for breast IDC grading. The experimental results show that the performance of the proposed method is better than that of state-of-the-art method. In addition, we released a well-labeled dataset with 3644 pathological images for breast IDC grading. This dataset is currently the largest publicly available breast IDC grading dataset and can serve as a benchmark to facilitate a broader study of breast IDC grading.
关键词	breast cancer grading histopathological image nuclei segmentation convolutional neural network attention mechanism
DOI	10.3390/s22114061
收录类别	SCI
语种	英语
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	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000809048500001
出版者	MDPI
引用统计	被引频次：7[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/2XEOYT63/19619
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Fa
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100045, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Boao Evergrande Int Hosp, Dept Pathol, Qionghai 571435, Peoples R China 4.Peking Univ Int Hosp, Dept Pathol, Beijing 100084, Peoples R China 5.Anhui Univ, Coll Comp Sci & Technol, Hefei 230093, Peoples R China
推荐引用方式 GB/T 7714	Yan, Rui,Ren, Fei,Li, Jintao,et al. Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images[J]. SENSORS,2022,22(11):15.
APA	Yan, Rui.,Ren, Fei.,Li, Jintao.,Rao, Xiaosong.,Lv, Zhilong.,...&Zhang, Fa.(2022).Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images.SENSORS,22(11),15.
MLA	Yan, Rui,et al."Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images".SENSORS 22.11(2022):15.