DECOR-Net: A Covid-19 Lung Infection Segmentation Network Improved by Emphasizing Low-Level Features and Decorrelating Features-Reference-Cited by-同舟云学术

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DECOR-Net: A Covid-19 Lung Infection Segmentation Network Improved by Emphasizing Low-Level Features and Decorrelating Features

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
language:
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Author:

Hu Jiesi¹,Yang Yanwu¹,Guo Xutao¹,Peng Bo²,Huang Hua³,Ma Ting¹

Affiliation:

1. Harbin Institute of Technology (Shenzhen),Electronic & Informatin Engineering School,Shenzhen,China

2. Institute of Pathogen Biology,Shenzhen Center for Disease Control and Prevention,Shenzhen,China,518055

3. The Third People’s Hospital of Shenzhen,Department of Radiology,China

Funder

Technology Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230325.pdf?arnumber=10230325

Reference19 articles.

1. Reducing overfitting in deep networks by decorrelating representations;cogswell,2015

2. Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review

3. Visualizing and Understanding Convolutional Networks

4. Orthogonal Convolutional Neural Networks

5. A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PDSMNet: Parallel pyramid dual-stream modeling for automatic lung COVID-19 infection segmentations;Engineering Applications of Artificial Intelligence;2024-07

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