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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: Deep learning with differential privacy. In: SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
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)
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)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lyu, M., Su, D., Li, N.: Understanding the sparse vector technique for differential privacy. Proc. VLDB Endow. 10(6), 637–648 (2017)
McMahan, B., et al.: Communication efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)
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_28
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_9
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: ICCV (2017)
Yu, H., Jin, R., Yang, S.: On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization. In: ICML (2019)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, W. et al. (2019). Privacy-Preserving Federated Brain Tumour Segmentation. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)