Advertisement

Privacy-Preserving Federated Brain Tumour Segmentation

  • Wenqi LiEmail author
  • Fausto Milletarì
  • Daguang Xu
  • Nicola Rieke
  • Jonny Hancox
  • Wentao Zhu
  • Maximilian Baust
  • Yan Cheng
  • Sébastien Ourselin
  • M. Jorge Cardoso
  • Andrew Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)

Abstract

Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.

Notes

Acknowledgements

We thank Rong Ou at NVIDIA for the helpful discussions.

The research was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), the Wellcome Flagship Programme (WT213038/Z/18/Z), the UKRI funded London Medical Imaging and AI centre for Value-based Healthcare, and the NIHR Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

References

  1. 1.
    Abadi, M., et al.: Deep learning with differential privacy. In: SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)Google Scholar
  2. 2.
    Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629 (2018)
  3. 3.
    Hitaj, B., Ateniese, G., Perez-Cruz, F.: Deep models under the GAN: information leakage from collaborative deep learning. In: SIGSAC Conference on Computer and Communications Security, pp. 603–618 (2017)Google Scholar
  4. 4.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  5. 5.
    Lyu, M., Su, D., Li, N.: Understanding the sparse vector technique for differential privacy. Proc. VLDB Endow. 10(6), 637–648 (2017)CrossRefGoogle Scholar
  6. 6.
    McMahan, B., et al.: Communication efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)Google Scholar
  7. 7.
    Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-11726-9_28CrossRefGoogle Scholar
  8. 8.
    Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 92–104. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-11723-8_9CrossRefGoogle Scholar
  9. 9.
    Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)Google Scholar
  10. 10.
    Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: ICCV (2017)Google Scholar
  11. 11.
    Yu, H., Jin, R., Yang, S.: On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization. In: ICML (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wenqi Li
    • 1
    Email author
  • Fausto Milletarì
    • 1
  • Daguang Xu
    • 1
  • Nicola Rieke
    • 1
  • Jonny Hancox
    • 1
  • Wentao Zhu
    • 1
  • Maximilian Baust
    • 1
  • Yan Cheng
    • 1
  • Sébastien Ourselin
    • 2
  • M. Jorge Cardoso
    • 2
  • Andrew Feng
    • 1
  1. 1.NVIDIASanta ClaraUSA
  2. 2.Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

Personalised recommendations