Mining multi-center heterogeneous medical data with distributed synthetic learning

Qi Chang, Zhennan Yan, Mu Zhou, Hui Qu, Xiaoxiao He, Han Zhang, Lohendran Baskaran, Subhi Al’Aref, Hongsheng Li (), Shaoting Zhang () and Dimitris N. Metaxas ()
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Qi Chang: Rutgers University
Zhennan Yan: SenseBrain Research
Mu Zhou: SenseBrain Research
Hui Qu: Rutgers University
Xiaoxiao He: Rutgers University
Han Zhang: Rutgers University
Lohendran Baskaran: National Heart Centre Singapore, and Duke-National University Of Singapore
Subhi Al’Aref: University of Arkansas for Medical Sciences
Hongsheng Li: Chinese University of Hong Kong
Shaoting Zhang: Shanghai Artificial Intelligence Laboratory
Dimitris N. Metaxas: Rutgers University

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.

Date: 2023
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DOI: 10.1038/s41467-023-40687-y

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