ArticlePattern recognition2021
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- MuGu:mutual guidance learning between pretrained SAM and lightweight model for medical image segmentation.Scientific reports · 2026Article
- An interpretable machine learning approach to prognosis of melioidosis pneumonia via computed tomography quantification and clinical data.Scientific reports · 2025Article
- Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans.Scientific reports · 2024Article
- Multiple myeloma segmentation net (MMNet): an encoder-decoder-based deep multiscale feature fusion model for multiple myeloma segmentation in magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- A new computed tomography score-based staging for melioidosis pneumonia to predict progression.Quantitative imaging in medicine and surgery · 2024Article
- SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation.Displays · 2023Article
- Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.Biomedical signal processing and control · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.Iranian journal of medical sciences · 2022Article
- Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT.Research in diagnostic and interventional imaging · 2022Article
- FAM: focal attention module for lesion segmentation of COVID-19 CT images.Journal of real-time image processing · 2022Article
- A deep-learning-based framework for severity assessment of COVID-19 with CT images.Expert systems with applications · 2021Article
- COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.Medical image analysis · 2021Article
- Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network.Frontiers in medicine · 2021Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.
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