A collaborative online AI engine for CT-based COVID-19 diagnosis

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data gener...

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Detalles Bibliográficos
Autores principales: Xu, Yongchao, Ma, Liya, Yang, Fan, Chen, Yanyan, Ma, Ke, Yang, Jiehua, Yang, Xian, Chen, Yaobing, Shu, Chang, Fan, Ziwei, Gan, Jiefeng, Zou, Xinyu, Huang, Renhao, Zhang, Changzheng, Liu, Xiaowu, Tu, Dandan, Xu, Chuou, Zhang, Wenqing, Yang, Dehua, Wang, Ming-Wei, Wang, Xi, Xie, Xiaoliang, Leng, Hongxiang, Holalkere, Nagaraj, Halin, Neil J., Kamel, Ihab Roushdy, Wu, Jia, Peng, Xuehua, Wang, Xiang, Shao, Jianbo, Mongkolwat, Pattanasak, Zhang, Jianjun, Rubin, Daniel L., Wang, Guoping, Zheng, Chuangsheng, Li, Zhen, Bai, Xiang, Xia, Tian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273252/
https://www.ncbi.nlm.nih.gov/pubmed/32511484
http://dx.doi.org/10.1101/2020.05.10.20096073

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273252/
https://www.ncbi.nlm.nih.gov/pubmed/32511484
http://dx.doi.org/10.1101/2020.05.10.20096073

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