FAM: focal attention module for lesion segmentation of COVID-19 CT images-Reference-Cited by-同舟云学术

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FAM: focal attention module for lesion segmentation of COVID-19 CT images

Published:2022-09-04 Issue:6 Volume:19 Page:1091-1104
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Wu Xiaoxin,Zhang Zhihao,Guo Lingling,Chen Hui,Luo Qiaojie,Jin Bei,Gu Weiyan,Lu Fangfang,Chen Jingjing^ORCID

Funder

Natural Science Foundation of Zhejiang Province

China Postdoctoral Science Foundation

Zhejiang Provincial Postdoctoral Science Foundation

the Educational Commission of Zhejiang Province of China

Publisher

Springer Science and Business Media LLC

Subject

Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11554-022-01249-5.pdf

Reference51 articles.

1. Ai, T., Yang, Z., Hou, H., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32–E40 (2020). https://doi.org/10.1148/radiol.2020200642

2. Adams, H.J., Kwee, T.C., Yakar, D., et al.: Chest CT imaging signature of coronavirus disease 2019 infection: in pursuit of the scientific evidence. Chest 158(5), 1885–1895 (2020). https://doi.org/10.1016/j.chest.2020.06.025

3. Xu, X., Tian, H., Zhang, X., Qi, L., He, Q., Dou, W.: DisCOV: distributed COVID-19 detection on X-ray images with edge-cloud collaboration. IEEE Trans. Serv. Comput. (2022). https://doi.org/10.1109/TSC.2022.3142265

4. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2016). https://doi.org/10.1109/TPAMI.2016.2572683

5. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/TPAMI.2016.2644615

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