Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s10278-021-00434-5.pdf
Reference53 articles.
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3. Kanne JP, Little BP, Chung JH, Elicker BM, Ketai LH. Essentials for Radiologists on COVID-19: An Update—Radiology Scientific Expert Panel. Radiology n.d.;0:200527. https://doi.org/10.1148/radiol.2020200527.
4. Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 2020;140:109761. https://doi.org/10.1016/j.mehy.2020.109761.
5. Chang T-H, Wu J-L, Chang L-Y. Clinical characteristics and diagnostic challenges of pediatric COVID-19: A systematic review and meta-analysis. J Formos Med Assoc 2020. https://doi.org/10.1016/j.jfma.2020.04.007.
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