Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI

TL;DR

This paper proposes an adaptive aggregation weight method for federated learning in pancreas MRI segmentation, improving cross-domain generalization and accuracy across multiple hospitals without compromising data privacy.

Contribution

It introduces a novel adaptive weighting scheme for federated model aggregation that accounts for domain differences, enhancing segmentation performance in heterogeneous medical datasets.

Findings

Improved segmentation accuracy across multiple hospitals.

Reduced impact of domain shift compared to traditional FL methods.

Enhanced model generalization in heterogeneous datasets.

Abstract

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).