Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast-Reference-Cited by-同舟云学术

Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast

Published:2022-12 Issue:12 Volume:41 Page:3611-3623
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Gao Zeyu¹^ORCID,Jia Chang¹,Li Yang¹,Zhang Xianli¹^ORCID,Hong Bangyang¹,Wu Jialun¹,Gong Tieliang¹^ORCID,Wang Chunbao²,Meng Deyu³^ORCID,Zheng Yefeng⁴^ORCID,Li Chen¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China

2. Department of Pathology, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, China

3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

4. Tencent Jarvis Laboratory, Shenzhen, China

Funder

Key Research and Development Program of Ningxia Hui Nationality Autonomous Region

Key Research and Development Program of Shaanxi Province

Innovative Research Group of the National Natural Science Foundation of China

National Natural Science Foundation of China

Consulting Research Project of the Chinese Academy of Engineering (The Online and Offline Mixed Educational Service System for The Belt and Road Training in Massive Open Online Courses

Project of China Knowledge Centre for Engineering Science and Technology

Innovation Team from the Ministry of Education

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/ielx7/42/9969446/09830779.pdf?arnumber=9830779

Reference46 articles.

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5. Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks

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