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Folly Beach Library
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Edgar Allan Poe/Sullivan's Island Library
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Wando Mount Pleasant Library
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Village Library
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St. Paul's/Hollywood Library
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Hurd/St. Andrews Library
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![loading](/sites/all/modules/hf_eds/images/loading.gif)
Focus, Fusion, and Rectify: Context-Aware Learning for COVID-19 Lung Infection Segmentation.
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![loading](/sites/all/modules/hf_eds/images/loading.gif)
- Author(s): Wang, Ruxin1 (AUTHOR) ; Ji, Chaojie1 (AUTHOR) ; Zhang, Yuxiao2 (AUTHOR) ; Li, Ye1 (AUTHOR)
- Source:
IEEE Transactions on Neural Networks & Learning Systems. Jan2022, Vol. 33 Issue 1, p12-24. 13p.- Subject Terms:
- Source:
- Additional Information
- Abstract: The coronavirus disease 2019 (COVID-19) pandemic is spreading worldwide. Considering the limited clinicians and resources and the evidence that computed tomography (CT) analysis can achieve comparable sensitivity, specificity, and accuracy with reverse-transcription polymerase chain reaction, the automatic segmentation of lung infection from CT scans supplies a rapid and effective strategy for COVID-19 diagnosis, treatment, and follow-up. It is challenging because the infection appearance has high intraclass variation and interclass indistinction in CT slices. Therefore, a new context-aware neural network is proposed for lung infection segmentation. Specifically, the autofocus and panorama modules are designed for extracting fine details and semantic knowledge and capturing the long-range dependencies of the context from both peer level and cross level. Also, a novel structure consistency rectification is proposed for calibration by depicting the structural relationship between foreground and background. Experimental results on multiclass and single-class COVID-19 CT images demonstrate the effectiveness of our work. In particular, our method obtains the mean intersection over union (mIoU) score of 64.8%, 65.2%, and 73.8% on three benchmark datasets for COVID-19 infection segmentation. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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