{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:17:13Z","timestamp":1768555033926,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA16021400"],"award-info":[{"award-number":["XDA16021400"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2021YFF0704300"],"award-info":[{"award-number":["2021YFF0704300"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["61932018"],"award-info":[{"award-number":["61932018"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["62072441"],"award-info":[{"award-number":["62072441"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["62072280"],"award-info":[{"award-number":["62072280"]}]},{"name":"National Key Research and Development Program of China","award":["XDA16021400"],"award-info":[{"award-number":["XDA16021400"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFF0704300"],"award-info":[{"award-number":["2021YFF0704300"]}]},{"name":"National Key Research and Development Program of China","award":["61932018"],"award-info":[{"award-number":["61932018"]}]},{"name":"National Key Research and Development Program of China","award":["62072441"],"award-info":[{"award-number":["62072441"]}]},{"name":"National Key Research and Development Program of China","award":["62072280"],"award-info":[{"award-number":["62072280"]}]},{"name":"NSFC projects","award":["XDA16021400"],"award-info":[{"award-number":["XDA16021400"]}]},{"name":"NSFC projects","award":["2021YFF0704300"],"award-info":[{"award-number":["2021YFF0704300"]}]},{"name":"NSFC projects","award":["61932018"],"award-info":[{"award-number":["61932018"]}]},{"name":"NSFC projects","award":["62072441"],"award-info":[{"award-number":["62072441"]}]},{"name":"NSFC projects","award":["62072280"],"award-info":[{"award-number":["62072280"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22114061","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Nuclei-Guided Network for Breast Cancer Grading in HE-Stained Pathological Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1336-1740","authenticated-orcid":false,"given":"Rui","family":"Yan","sequence":"first","affiliation":[{"name":"High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Fei","family":"Ren","sequence":"additional","affiliation":[{"name":"High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China"}]},{"given":"Jintao","family":"Li","sequence":"additional","affiliation":[{"name":"High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China"}]},{"given":"Xiaosong","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Pathology, Boao Evergrande International Hospital, Qionghai 571435, China"},{"name":"Department of Pathology, Peking University International Hospital, Beijing 100084, China"}]},{"given":"Zhilong","family":"Lv","sequence":"additional","affiliation":[{"name":"High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China"}]},{"given":"Chunhou","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Anhui University, Hefei 230093, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2081-9369","authenticated-orcid":false,"given":"Fa","family":"Zhang","sequence":"additional","affiliation":[{"name":"High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","article-title":"Pathological prognostic factors in breast cancer. 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