Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network-Reference-Cited by-同舟云学术

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Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network

Published:2022-10-01 Issue:10 Volume:29 Page:1095-1103
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Xin Bin¹^ORCID,Yang Yaning¹,Xie Xiaolan²,Shang Jiandong³,Liu Zhengyu⁴⁵,Peng Shaoliang¹

Affiliation:

1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.

2. College of Information Science and Engineering, Guilin University of Technology, Guilin, China.

3. National Supercomputing Center in Zhengzhou, Zhengzhou University, Henan, China.

4. Department of Cardiology, Hunan Provincial People's Hospital, Changsha, China.

5. Department of Epidemiological Research, Hunan Provincial People's Hospital, Changsha, China.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2022.0111

Reference19 articles.

1. Some Forms of Trust

2. Chen, H., Dou, Q., Wang, X., et al. 2016. Mitosis detection in breast cancer histology images via deep cascaded networks. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.

3. Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis

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