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Using federated data sources and Varian Learning Portal framework to train a neural network model for automatic organ

Elena Czeizler1, Wolfgang Wiessler2, Thorben Koester2

  • 1Varian Medical Systems Finland Oy, Paciuksenkatu 21, FI-00270 Helsinki, Finland.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|March 21, 2020
PubMed
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A deep neural network achieved comparable performance for female pelvic organ segmentation using distributed, privacy-preserving federated learning. This approach enables multi-site training without sharing sensitive patient data.

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Radiotherapy

Background:

  • Federated learning enables multi-institutional research without direct data sharing.
  • Privacy-preserving machine learning is crucial for sensitive medical data.
  • Accurate organ segmentation is vital for radiotherapy planning.

Purpose of the Study:

  • To train a deep neural network for female pelvic organ segmentation using a distributed framework.
  • To evaluate the prediction power of a federated learning model against a centralized model.
  • To assess the feasibility of the Varian Learning Portal (VLP) for privacy-preserving medical research.

Main Methods:

  • Utilized the Varian Learning Portal (VLP), a distributed machine learning infrastructure.
  • Trained a deep neural network model for female pelvic organ segmentation across multiple hospital sites.
Keywords:
Convolutional Neural NetworkDistributed TrainingFederated Data SourcesFemale Pelvis Organ SegmentationVarian Learning Portal

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  • Employed a synchronous data distributed framework for privacy-preserving model training.

    • Main Results:

    • The federated learning model achieved prediction performance on par with a centralized training model.
    • The VLP infrastructure facilitated effective GPU-based training for complex organ segmentation.
    • The study demonstrated successful organ segmentation for the female pelvic region.

    Conclusions:

    • The Varian Learning Portal (VLP) supports privacy-preserving, GPU-based deep learning for organ segmentation.
    • Federated learning via VLP allows training on diverse datasets from multiple clinics without compromising patient privacy.
    • This approach is effective for challenging segmentation tasks like the female pelvic region, improving model robustness.