Privacy-Preserving Federated Brain Tumour Segmentation-Reference-Cited by-同舟云学术

Privacy-Preserving Federated Brain Tumour Segmentation

Published:2019 Issue: Volume: Page:133-141
ISSN:0302-9743
Container-title:Machine Learning in Medical Imaging
language:
Short-container-title:

Author:

Li Wenqi,Milletarì Fausto,Xu Daguang,Rieke Nicola,Hancox Jonny,Zhu Wentao,Baust Maximilian,Cheng Yan,Ourselin Sébastien,Cardoso M. Jorge,Feng Andrew

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-32692-0_16

Reference11 articles.

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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. 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)

4. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

5. Lyu, M., Su, D., Li, N.: Understanding the sparse vector technique for differential privacy. Proc. VLDB Endow. 10(6), 637–648 (2017)

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