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

@article{Bai2021AdvancingCD,
  title={Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence},
  author={Xiang Bai and Hanchen Wang and Liya Ma and Yongchao Xu and Jiefeng Gan and Ziwei Fan and Fan Yang and Ke Ma and Jiehua Yang and Song Bai and Chang Shu and Xinyu Zou and Renhao Huang and Changzheng Zhang and Xiaowu Liu and Dandan Tu and Chuou Xu and Wenqing Zhang and Xi Le Wang and Anguo Chen and Yu Zeng and Dehua Yang and Ming-Wei Wang and Nagaraj Setty Holalkere and Neil J. Halin and Ihab R. Kamel and Jia Wu and Xue-Hua Peng and Xiang Wang and Jianbo Shao and Pattanasak Mongkolwat and Jianjun Zhang and Weiyang Liu and Michael Roberts and Zhongzhao Teng and Lucian Beer and Lorena E. Sanchez and Evis Sala and D. Rubin and Adrian Weller and Joan Lasenby and Chuangsheng Zheng and Jianming Wang and Zhen Li and Carola-Bibiane Schonlieb and Tian Xia},
  journal={ArXiv},
  year={2021}
}
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 clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution… 
Machine Learning Generalizability Across Healthcare Settings: Insights from multi-site COVID-19 screening
TLDR
Three methods for translating ready-made models for adoption in new settings by readingjusting the decision threshold on the output of a ready- made model using site-specific data via transfer learning are introduced and it is demonstrated that site- specific customization improves predictive performance when compared to otherready-made approaches.

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