Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi- Center Scenarios

Hanwen Zhang , Mingzhi Chen , Yuxi Liu

IEEE Journal of Biomedical and Health Informatics +

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March 27, 2025

View abstract on PubMed

Summary

This study introduces a privacy-preserving framework for federated learning on Non-Independent and Identically Distributed (Non-IID) medical data. The approach enhances medical image segmentation models with improved accuracy and reduced communication costs.

Area Of Science

Artificial Intelligence
Medical Imaging
Machine Learning

Background

Federated learning (FL) faces challenges in multi-center medical datasets due to data heterogeneity and privacy concerns.
Existing FL methods struggle with Non-Independent and Identically Distributed (Non-IID) data and incur high communication costs.

Purpose Of The Study

To propose a practical privacy-preserving framework for training Non-IID medical image segmentation models in multi-center settings with low communication overhead.
To mitigate data heterogeneity and improve the efficiency of federated learning in healthcare.

Main Methods

Developed an efficient cascaded diffusion model to generate synthetic image-mask pairs, addressing data heterogeneity.
Implemented a label construction module to enhance the quality of generated data.
Proposed aggregation methods (CD-Syn, CD-Ens, CD-KD) for diverse scenarios, balancing efficiency, privacy, and accuracy.

Main Results

The framework achieved an average improvement of 5.38% in Dice score compared to the baseline FednnU-Net across five Non-IID medical datasets.
Demonstrated effectiveness in practical multi-center settings with varying demands for privacy and accuracy.
Successfully reduced communication costs while maintaining high performance.

Conclusions

The proposed privacy-preserving framework offers a flexible and effective solution for Non-IID medical image segmentation using federated learning.
The cascaded diffusion model and novel aggregation strategies significantly improve model performance and data utility.
This approach provides a practical pathway for leveraging multi-center medical data securely and efficiently.