Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning

Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning

Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient’s paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky...

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Bibliographic Details
Main Authors: Hong Liu, Wen-Dong Xu, Zi-Hao Shang, Xiang-Dong Wang, Hai-Yan Zhou, Ke-Wen Ma, Huan Zhou, Jia-Lin Qi, Jia-Rui Jiang, Li-Lan Tan, Hui-Min Zeng, Hui-Juan Cai, Kuan-Song Wang, Yue-Liang Qian
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-04-01
Series:Frontiers in Oncology
Subjects:
pathological image
weakly supervised learning
molecular subtype
breast cancer
H&E
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.858453/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2022.858453/full

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