Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

Abstract Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc....

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Autores principales:	Zhongyi Han, Benzheng Wei, Yuanjie Zheng, Yilong Yin, Kejian Li, Shuo Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9f36355affc84a4c955336532a56765f
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spelling	oai:doaj.org-article:9f36355affc84a4c955336532a56765f2021-12-02T16:06:46ZBreast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model10.1038/s41598-017-04075-z2045-2322https://doaj.org/article/9f36355affc84a4c955336532a56765f2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04075-zhttps://doaj.org/toc/2045-2322Abstract Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.Zhongyi HanBenzheng WeiYuanjie ZhengYilong YinKejian LiShuo LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhongyi Han Benzheng Wei Yuanjie Zheng Yilong Yin Kejian Li Shuo Li Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
description	Abstract Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.
format	article
author	Zhongyi Han Benzheng Wei Yuanjie Zheng Yilong Yin Kejian Li Shuo Li
author_facet	Zhongyi Han Benzheng Wei Yuanjie Zheng Yilong Yin Kejian Li Shuo Li
author_sort	Zhongyi Han
title	Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
title_short	Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
title_full	Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
title_fullStr	Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
title_full_unstemmed	Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model
title_sort	breast cancer multi-classification from histopathological images with structured deep learning model
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9f36355affc84a4c955336532a56765f
work_keys_str_mv	AT zhongyihan breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel AT benzhengwei breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel AT yuanjiezheng breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel AT yilongyin breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel AT kejianli breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel AT shuoli breastcancermulticlassificationfromhistopathologicalimageswithstructureddeeplearningmodel
_version_	1718384859021312000

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

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