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

• Published in: Scientific reports Vol. 7; no. 1; pp. 4172 - 10
• Main Authors: Han, Zhongyi, Wei, Benzheng, Zheng, Yuanjie, Yin, Yilong, Li, Kejian, Li, Shuo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.06.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/705/117; Automation; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - pathology; Classification; Databases as Topic; Deep Learning; Female; Fibroadenoma; Humanities and Social Sciences; Humans; Mammography; Models, Theoretical; multidisciplinary; Neural Networks (Computer); Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-04075-z

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.