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Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data

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Machine Learning in Medical Imaging (MLMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10541))

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Abstract

Medical image data is naturally distributed among clinical institutions. This partitioning, combined with security and privacy restrictions on medical data, imposes limitations on machine learning algorithms in clinical applications, especially for small and newly established institutions. We present InsuLearn: an intuitive and robust open-source (open-source code available at: https://github.com/DistributedML/InsuLearn) platform designed to facilitate distributed learning (classification and regression) on medical image data, while preserving data security and privacy. InsuLearn is built on ensemble learning, in which statistical models are developed at each institution independently and combined at secure coordinator nodes. InsuLearn protocols are designed such that the liveness of the system is guaranteed as institutions join and leave the network. Coordination is implemented as a cluster of replicated state machines, making it tolerant to individual node failures. We demonstrate that InsuLearn successfully integrates accurate models for horizontally partitioned data while preserving privacy.

This work is supported in part by the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC.

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Notes

  1. 1.

    Open-source code available at: https://github.com/DistributedML/InsuLearn.

  2. 2.

    In fact \(h_i\) does not know the size of H nor the nodes in H.

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Author information

Authors and Affiliations

  1. Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, Canada

    Alborz Amir-Khalili & Rafeef Abugharbieh

  2. Computer Science, University of British Columbia, Vancouver, Canada

    Soheil Kianzad & Ivan Beschastnikh

Authors
  1. Alborz Amir-Khalili
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  2. Soheil Kianzad
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  3. Rafeef Abugharbieh
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  4. Ivan Beschastnikh
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Corresponding author

Correspondence to Alborz Amir-Khalili .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Qian Wang

  2. Nanjing University , Nanjing, China

    Yinghuan Shi

  3. Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

  4. Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

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Amir-Khalili, A., Kianzad, S., Abugharbieh, R., Beschastnikh, I. (2017). Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-67389-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67388-2

  • Online ISBN: 978-3-319-67389-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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