1 code implementation • 27 Jun 2022 • Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.
1 code implementation • 27 May 2022 • Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, Qi Dou
Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.
Histopathological Image Classification Image Classification +2
1 code implementation • 16 Apr 2022 • Meirui Jiang, Hongzheng Yang, Chen Cheng, Qi Dou
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.
1 code implementation • 16 Jun 2021 • Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng
This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i. e., hospitals).