Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
This work addresses domain generalization challenges in federated learning for medical imaging, specifically for pancreas segmentation across hospitals, representing an incremental improvement over existing FL methods.
The paper tackled the problem of domain shift in federated learning for pancreas MRI segmentation by introducing adaptive aggregation weights, which improved segmentation accuracy and reduced domain shift effects compared to traditional methods like FedAvg.
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers).