Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in cl...

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Detalles Bibliográficos
Autores principales: Bai, Xiang, Wang, Hanchen, Ma, Liya, Xu, Yongchao, Gan, Jiefeng, Fan, Ziwei, Yang, Fan, Ma, Ke, Yang, Jiehua, Bai, Song, Shu, Chang, Zou, Xinyu, Huang, Renhao, Zhang, Changzheng, Liu, Xiaowu, Tu, Dandan, Xu, Chuou, Zhang, Wenqing, Wang, Xi, Chen, Anguo, Zeng, Yu, Yang, Dehua, Wang, Ming-Wei, Holalkere, Nagaraj, Halin, Neil J., Kamel, Ihab R., Wu, Jia, Peng, Xuehua, Wang, Xiang, Shao, Jianbo, Mongkolwat, Pattanasak, Zhang, Jianjun, Liu, Weiyang, Roberts, Michael, Teng, Zhongzhao, Beer, Lucian, Escudero Sanchez, Lorena, Sala, Evis, Rubin, Daniel, Weller, Adrian, Lasenby, Joan, Zheng, Chuangsheng, Wang, Jianming, Li, Zhen, Schönlieb, Carola-Bibiane, Xia, Tian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609899/
https://www.ncbi.nlm.nih.gov/pubmed/34815983

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609899/
https://www.ncbi.nlm.nih.gov/pubmed/34815983

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