Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence

Abstract The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces ob...

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Бібліографічні деталі
Автори:	Bao Feng, Jiangfeng Shi, Liebin Huang, Zhiqi Yang, Shi-Ting Feng, Jianpeng Li, Qinxian Chen, Huimin Xue, Xiangguang Chen, Cuixia Wan, Qinghui Hu, Enming Cui, Yehang Chen, Wansheng Long
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Nature Portfolio 2024-01-01
Серія:	Nature Communications
Онлайн доступ:	https://doi.org/10.1038/s41467-024-44946-4
Теги:	Додати тег Немає тегів, Будьте першим, хто поставить тег для цього запису!

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence

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