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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Privacy-preserving Collaborative Training for Medical Image Analysis Based on Multi-Blockchain.

Wanlu Zhang1, Qigang Wang1, Mei Li1

  • 1AI Lab, Lenovo, Beijing, China.

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|October 23, 2020
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Summary

This study introduces a multi-Blockchain decentralized method for collaborative machine learning in medical imaging, protecting patient data privacy. The approach enables secure model training across institutions without data sharing, enhancing accuracy and accelerating convergence.

Keywords:
Blockchaindeep learningdistributed trainingmedical image analysis.personalized learningtransfer learning

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Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • Growing concerns regarding patient medical data privacy.
  • Rapid advancements in artificial intelligence and big data analysis.

Purpose of the Study:

  • To introduce a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis.
  • To strengthen data protection while ensuring effective model training.
  • To enable collaborative model training among researchers from different institutions without sensitive data exchange.

Main Methods:

  • Utilizing a multi-Blockchain system for decentralized collaborative machine learning.
  • Implementing a partial parameter update method to prevent privacy leakage.
  • Employing peer-to-peer communication for leveraging auxiliary information from similar tasks on other Blockchains.
  • Conducting personalized model training for individual medical institutions.

Main Results:

  • The proposed method achieves performance comparable to centralized training while preventing private data leakage.
  • Transferring auxiliary information accelerates model convergence and improves accuracy, particularly in data-scarce scenarios.
  • Personalized training further enhances model performance.

Conclusions:

  • The developed approach facilitates effective collaborative training for researchers across organizations.
  • It ensures the protection of private patient data during the machine learning model training process.