# Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI

arXiv: https://arxiv.org/abs/2410.22530 · HTML: https://mlsanity.com/paper/2410.22530/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FedALA: Adaptive Local Aggregation for Personalized Federated Learning](https://mlsanity.com/paper/2212.01197/) (2022)
- [FedAvg with Fine Tuning: Local Updates Lead to Representation Learning](https://mlsanity.com/paper/2205.13692/) (2022)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation](https://mlsanity.com/paper/2203.06338/) (2022)
- [DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates](https://mlsanity.com/paper/2307.07652/) (2023)
- [SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation](https://mlsanity.com/paper/2310.17764/) (2023)
- [Federated Momentum Contrastive Clustering](https://mlsanity.com/paper/2206.05093/) (2022)
- [Frequency-Based Federated Domain Generalization for Polyp Segmentation](https://mlsanity.com/paper/2410.02044/) (2024)

## Built on by (most cited first, 5 of 5)

- [Pancreas Part Segmentation under Federated Learning Paradigm](https://mlsanity.com/paper/2509.23562/) (2025)
- [Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation](https://mlsanity.com/paper/2506.23334/) (2025)
- [Mix-modal Federated Learning for MRI Image Segmentation](https://mlsanity.com/paper/2509.02541/) (2025)
- [Personalized Federated Segmentation with Shared Feature Aggregation and Boundary-Focused Calibration](https://mlsanity.com/paper/2511.18847/) (2025)
- [Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer](https://mlsanity.com/paper/2609.13043/) (2026)

## Abstract (arXiv)

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).