中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images

文献类型：期刊论文

作者Yan, Rui1,2; Ren, Fei2; Li, Jintao2; Rao, Xiaosong3,4; Lv, Zhilong2; Zheng, Chunhou5; Zhang, Fa2
刊名SENSORS
出版日期2022-06-01
卷号22期号:11页码:15
关键词breast cancer grading histopathological image nuclei segmentation convolutional neural network attention mechanism
DOI10.3390/s22114061
英文摘要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.
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
语种英语
出版者MDPI
WOS记录号WOS:000809048500001
源URL[http://119.78.100.204/handle/2XEOYT63/19618]  
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.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100045, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。