COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automat...

Descripción completa

Detalles Bibliográficos
Autores principales: Bao, Guoqing, Chen, Huai, Liu, Tongliang, Gong, Guanzhong, Yin, Yong, Wang, Lisheng, Wang, Xiuying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666107/
https://www.ncbi.nlm.nih.gov/pubmed/34924632
http://dx.doi.org/10.1016/j.patcog.2021.108499

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666107/
https://www.ncbi.nlm.nih.gov/pubmed/34924632
http://dx.doi.org/10.1016/j.patcog.2021.108499

Ejemplares similares