Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
ObjectivesCoronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease pro...
Main Authors: | Lu-shan Xiao, Pu Li, Fenglong Sun, Yanpei Zhang, Chenghai Xu, Hongbo Zhu, Feng-Qin Cai, Yu-Lin He, Wen-Feng Zhang, Si-Cong Ma, Chenyi Hu, Mengchun Gong, Li Liu, Wenzhao Shi, Hong Zhu |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-07-01
|
Series: | Frontiers in Bioengineering and Biotechnology |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00898/full |
Similar Items
-
Clinical Characteristics of Patients With Progressive and Non-progressive Coronavirus Disease 2019: Evidence From 365 Hospitalised Patients in Honghu and Nanchang, China
by: Yanpei Zhang, et al.
Published: (2020-11-01) -
A multi-instance multi-label improved algorithm based on semi-supervised learning
by: Li Cunhe, et al.
Published: (2019-07-01) -
Localized instance fusion of MRI data of Alzheimer’s disease for classification based on instance transfer ensemble learning
by: Xiaoheng Tan, et al.
Published: (2018-05-01) -
Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine
by: Peipei Wang, et al.
Published: (2020-01-01) -
Multiple-instance learning with pairwise instance similarity
by: Yuan Liming, et al.
Published: (2014-09-01)