ArticlePattern recognition2021
Joint segmentation and detection of COVID-19 via a sequential region generation network.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images.Biomedical signal processing and control · 2023Article
- COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023Article
- Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.Concurrency and computation : practice & experience · 2022Article
- COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
- AI-Based human audio processing for COVID-19: A comprehensive overview.Pattern recognition · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
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Authors and funding
10 authors.
Funding
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Abstract
The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.
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Registered trials
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