Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several...

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Bibliographic Details
Main Authors: Chen, C. (Author), Chen, Q. (Author), Chen, Y.-W (Author), Hu, H. (Author), Iwamoto, Y. (Author), Kitrungrotsakul, T. (Author), Li, J. (Author), Lin, L. (Author), Tong, R. (Author), Wu, H. (Author), Xu, F. (Author), Zhou, Y. (Author), Zhu, W. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
accuracy
Article
attention
clinical trial
computer assisted diagnosis
computer assisted tomography
Computerized tomography
coronavirus disease 2019
COVID-19
CT scan
Databases, Factual
deep learning
Deep learning
Deep Learning
Diagnosis
diagnostic imaging
evaluation study
factual database
human
Humans
image segmentation
Image segmentation
Important features
Initial segmentation
interactive
Interactive refinement
Learning-based segmentation
lung
Lung
multicenter study
pneumonia
procedures
Radiographic Image Interpretation, Computer-Assisted
refinement
seed point
segmentation
Segmentation accuracy
Shape and size
State of the art
Tomography, X-Ray Computed
training
Training dataset
validation process
x-ray computed tomography
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