The Contrastive Network With Convolution and Self-Attention Mechanisms for Unsupervised Cell Segmentation-Reference-Cited by-同舟云学术

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The Contrastive Network With Convolution and Self-Attention Mechanisms for Unsupervised Cell Segmentation

Published:2023-12 Issue:12 Volume:27 Page:5837-5847
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Zhao Yuhang¹^ORCID,Shao Xianhao²^ORCID,Chen Cai³^ORCID,Song Junlin¹^ORCID,Tian Chongxuan¹^ORCID,Li Wei¹^ORCID

Affiliation:

1. Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, China

2. Department of Orthopaedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

3. Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shandong Provincial Key Research and Development Program

Major Scientific and Technological Innovation Project

Natural Science Foundation of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10345388/10235953.pdf?arnumber=10235953

Reference66 articles.

1. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma

2. AI-based pathology predicts origins for cancers of unknown primary

3. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

4. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

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

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