Dan Wang, Chu Han, Zhen Zhang, Tiantian Zhai, Huan Lin, Baoyao Yang, Yanfen Cui, Yinbing Lin, Zhihe Zhao, Lujun Zhao, Changhong Liang, An Zeng, Dan Pan, Xin Chen, Zhenwei Shi, Zaiyi Liu
BACKGROUND AND OBJECTIVE: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors...
March 25, 2024: Computer Methods and Programs in Biomedicine