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Mining multi-center heterogeneous medical data with distributed synthetic learning.

Qi Chang¹, Zhennan Yan², Mu Zhou^2,3

¹Department of Computer Science, Rutgers University, Piscataway, NJ, USA.

Nature Communications

|September 7, 2023

Summary

Distributed Synthetic Learning (DSL) creates high-quality synthetic medical images from multi-center data, overcoming privacy and heterogeneity challenges for improved medical analytics. This approach enhances data analysis across diverse healthcare systems.

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Area of Science:

Medical Imaging Analytics
Artificial Intelligence in Healthcare
Data Privacy and Security

Background:

Multi-center medical data analytics face significant hurdles due to patient privacy concerns and data heterogeneity across healthcare institutions.
Existing methods struggle to effectively integrate and analyze data from various sources while maintaining data integrity and privacy.

Purpose of the Study:

To introduce the Distributed Synthetic Learning (DSL) architecture for secure, cross-institutional medical data analysis.
To enable the creation of homogeneous synthetic medical image datasets for robust machine learning model training.
To address challenges of multi-modality learning, missing data, and continual learning in medical analytics.

Main Methods:

Developed the Distributed Synthetic Learning (DSL) architecture utilizing Generative Adversarial Network (GAN)-based synthetic learning.
Implemented key functionalities including multi-modality learning, missing modality completion, and continual learning within the DSL framework.
Evaluated DSL performance on cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets.

Main Results:

DSL successfully generated high-quality synthetic medical images, validated by the Dist-FID metric.
The architecture demonstrated adaptability to heterogeneous datasets.
DSL significantly outperformed existing models, achieving a 55% improvement in segmentation for real misaligned modalities and an 8% improvement for temporal datasets.

Conclusions:

The Distributed Synthetic Learning (DSL) architecture effectively overcomes privacy and heterogeneity barriers in multi-center medical data analysis.
DSL serves as a superior provider of synthetic medical images, enabling enhanced performance in various medical applications.
The proposed method offers a scalable and secure solution for leveraging distributed medical data for advanced analytics.