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-Reference-Cited by-同舟云学术

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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

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Liu Jinping¹^ORCID,Zhao Junqi¹^ORCID,Cen Lihui²^ORCID,Wu Xiaoqiang¹^ORCID,Wu Juanjuan¹^ORCID,Zhang Kun³^ORCID,Zhou Ying⁴^ORCID

Affiliation:

1. College of Information Science and Engineering, Hunan Normal University, Changsha, China

2. School of Automation, Central South University, Changsha, China

3. Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, Changsha, China

4. Data and Information Management Center, Hunan Children’s Hospital, Changsha, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10530134.pdf?arnumber=10530134

Reference42 articles.

1. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

2. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

3. HFCF‐Net: A hybrid‐feature cross fusion network for COVID‐19 lesion segmentation from CT volumetric images

4. COVID-19 detection using X-ray images and statistical measurements

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