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Mining multi-center heterogeneous medical data with distributed synthetic learning

Author

Listed:
  • Qi Chang

    (Rutgers University)

  • Zhennan Yan

    (SenseBrain Research)

  • Mu Zhou

    (SenseBrain Research
    Shanghai Artificial Intelligence Laboratory)

  • 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
    Centre for Perceptual and Interactive Intelligence (CPII))

  • Shaoting Zhang

    (Shanghai Artificial Intelligence Laboratory
    Centre for Perceptual and Interactive Intelligence (CPII)
    SenseTime)

  • Dimitris N. Metaxas

    (Rutgers University)

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%.

Suggested Citation

  • Qi Chang & Zhennan Yan & Mu Zhou & Hui Qu & Xiaoxiao He & Han Zhang & Lohendran Baskaran & Subhi Al’Aref & Hongsheng Li & Shaoting Zhang & Dimitris N. Metaxas, 2023. "Mining multi-center heterogeneous medical data with distributed synthetic learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40687-y
    DOI: 10.1038/s41467-023-40687-y
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    References listed on IDEAS

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    1. Sarthak Pati & Ujjwal Baid & Brandon Edwards & Micah Sheller & Shih-Han Wang & G. Anthony Reina & Patrick Foley & Alexey Gruzdev & Deepthi Karkada & Christos Davatzikos & Chiharu Sako & Satyam Ghodasa, 2022. "Federated learning enables big data for rare cancer boundary detection," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Papanicolas, Irene & Woskie, Liana R. & Jha, Ashish K., 2018. "Health care spending in the United States and other high-income countries," LSE Research Online Documents on Economics 87362, London School of Economics and Political Science, LSE Library.
    3. Chao Yan & Yao Yan & Zhiyu Wan & Ziqi Zhang & Larsson Omberg & Justin Guinney & Sean D. Mooney & Bradley A. Malin, 2022. "A Multifaceted benchmarking of synthetic electronic health record generation models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
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    2. Bin Du & Xiumin Liu & Junlong Zhao, 2024. "Extended Hotelling $$T^2$$ T 2 test in distributed frameworks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1160-1179, December.

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