CdcSegNet: Automatic COVID-19 Infection Segmentation From CT Images-Reference-Cited by-同舟云学术

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CdcSegNet: Automatic COVID-19 Infection Segmentation From CT Images

Published:2023 Issue: Volume:72 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Ju¹^ORCID,Chen Dechen²^ORCID,Ma Dong³^ORCID,Ying Changgang³^ORCID,Sun Xiaoyan¹^ORCID,Xu Xiaobing⁴^ORCID,Cheng Yun¹^ORCID

Affiliation:

1. College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China

2. Hundsun Technologies Inc., Hangzhou, China

3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

4. Department of Medical Imaging, Zhejiang Hospital, Hangzhou, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10102560.pdf?arnumber=10102560

Reference41 articles.

1. LGAN: Lung segmentation in CT scans using generative adversarial network

2. F³Net: Fusion, Feedback and Focus for Salient Object Detection

3. Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

4. Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

5. DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images

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3. GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation;Medical & Biological Engineering & Computing;2024-02-03

4. Automated Segmentation of COVID-19 Lung Infections From Chest CT Images via 2.5-D Dual-Path Encoder With Multiscale Dynamic Weight Deep Supervision Mechanism;IEEE Transactions on Instrumentation and Measurement;2024

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