Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation-Reference-Cited by-同舟云学术

Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation

Published:2023-10 Issue:10 Volume:42 Page:3104-3116
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Zhou Yang¹^ORCID,Wu Yongjian¹,Wang Zihua¹^ORCID,Wei Bingzheng²^ORCID,Lai Maode³,Shou Jianzhong⁴,Fan Yubo¹^ORCID,Xu Yan¹^ORCID

Affiliation:

1. School of Biological Science and Medical Engineering, State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China

2. Xiaomi Corporation, Beijing, China

3. Department of Pathology, School of Medicine, Zhejiang Provincial Key Laboratory of Disease Proteomics, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou, China

4. Department of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Chaoyang, China

Funder

National Natural Science Foundation in China

Beijing Natural Science Foundation-Haidian District Joint

Beijing Hope Run Special Fund of Cancer Foundation of China

Fundamental Research Funds for the Central Universities of China from the State Key Laboratory of Software Development Environment, Beihang University, China

111 Project in China

High Performance Computing (HPC) Resources at Beihang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10269089/10124051.pdf?arnumber=10124051

Reference79 articles.

1. Deep neural network models for computational histopathology: A survey

2. Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential

3. Mask-RCNN and U-Net Ensembled for Nuclei Segmentation

4. CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation

5. Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations

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