Search Results for author:
Found 4 papers, 4 papers with code
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
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.
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
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
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
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).
