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 obstacles related to privacy protection. Therefore, the present study aims to establish a robust federated learning model to overcome the data island problem and identify high-risk patients with postoperative gastric cancer recurrence in a multicentre, cross-institution setting, thereby enabling robust treatment with significant value. In the present study, we collect data from four independent medical institutions for experimentation. The robust federated learning model algorithm yields area under the rec

SUBMITTER: Feng B 

PROVIDER: S-EPMC10811238 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature