Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning-Reference-Cited by-同舟云学术

Annotation-Efficient COVID-19 Pneumonia Lesion Segmentation Using Error-Aware Unified Semisupervised and Active Learning

Published:2023-04 Issue:2 Volume:4 Page:255-267
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Fang Zhenghan¹^ORCID,Bai Junjie¹^ORCID,Guo Xinyu¹^ORCID,Wang Xin¹,Gao Feng¹,Yang Hao-Yu¹,Kong Bin¹,Hou Ying¹,Cao Kunlin¹,Song Qi¹,Xia Jun²,Yin Youbing¹^ORCID

Affiliation:

1. Keya Medical Company, Ltd., Shenzhen, China

2. Shenzhen Second People’s Hospital, Shenzhen, China

Funder

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Link

http://xplorestaging.ieee.org/ielx7/9078688/10081152/09699409.pdf?arnumber=9699409

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4. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

5. Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019

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