Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance-Reference-Cited by-同舟云学术

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Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance

Published:2023-12-12 Issue: Volume: Page:
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Sun Kai,Zheng Yuanjie,Yang Xinbo,Chen Xinyuan,Jia Weikuan

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

the New Twentieth Items of Universities in Jinan

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-023-02970-4.pdf

Reference33 articles.

1. Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, Zeng H, Zhou J, Wei W (2021) Global patterns of breast cancer incidence and mortality: a population-based cancer registry data analysis from 2000 to 2020. Cancer Commun 41(11):1183–1194

2. Zuo T-T, Zheng R-S, Zeng H-M, Zhang S-W (2013) Chen W-Q (2017) Female breast cancer incidence and mortality in China. Thoracic Cancer 8(3):214–218

3. Reshma V, Arya N, Ahmad SS, Wattar I, Mekala S, Joshi S, Krah D et al (2022) Detection of breast cancer using histopathological image classification dataset with deep learning techniques. BioMed Research International 2022

4. Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S (2010) Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 102(16):1224–1237

5. Urbaniak C, Gloor GB, Brackstone M, Scott L, Tangney M, Reid G (2016) The microbiota of breast tissue and its association with breast cancer. Appl Environ Microbiol 8(16):5039–5048

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