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Privacy-Preserving Federated Brain Tumour Segmentation

Part of the Lecture Notes in Computer Science book series (LNIP,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.

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Notes

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    https://devblogs.nvidia.com/annotate-adapt-model-medical-imaging-clara-train-sdk/.

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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.

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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

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  • DOI: https://doi.org/10.1007/978-3-030-32692-0_16

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