Cargando…
Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19
BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT ima...
Autores principales: | Fung, Daryl L. X., Liu, Qian, Zammit, Judah, Leung, Carson Kai-Sang, Hu, Pingzhao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312213/ https://www.ncbi.nlm.nih.gov/pubmed/34311742 http://dx.doi.org/10.1186/s12967-021-02992-2 |
Ejemplares similares
-
Semi-supervised COVID-19 CT image segmentation using deep generative models
por: Zammit, Judah, et al.
Publicado: (2022) -
Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images
por: Liu, Xiaoming, et al.
Publicado: (2022) -
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA
por: Liu, Qian, et al.
Publicado: (2021) -
A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data
por: Fung, Daryl L X, et al.
Publicado: (2023) -
MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19
Publicado: (2021)