Privacy-Preserving Federated Brain Tumour Segmentation

TY - CHAP

T1 - Privacy-Preserving Federated Brain Tumour Segmentation

AU - Li, Wenqi

AU - Milletarì, Fausto

AU - Xu, Daguang

AU - Rieke, Nicola

AU - Hancox, Jonny

AU - Zhu, Wentao

AU - Baust, Maximilian

AU - Cheng, Yan

AU - Ourselin, Sébastien

AU - Cardoso, M. Jorge

AU - Feng, Andrew

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85075695218&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32692-0_16

DO - 10.1007/978-3-030-32692-0_16

M3 - Conference paper

AN - SCOPUS:85075695218

SN - 9783030326913

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 133

EP - 141

BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings

A2 - Suk, Heung-Il

A2 - Liu, Mingxia

A2 - Lian, Chunfeng

A2 - Yan, Pingkun

PB - SPRINGER

T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 13 October 2019

ER -