Privacy-preserving Federated Brain Tumour Segmentation

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Original

2014

Earlier work this paper cites.

Shokri, R., Shmatikov, V.: Privacy-Preserving Deep Learning. In: SIGSAC Conference on Computer and Communications Security. pp. 1310–1321 (2015)

2015

Earlier work this paper cites.

Abadi, M., et al.: Deep Learning with Differential Privacy. SIGSAC Conference on Computer and Communications Security pp. 308–318 (2016)

2016

Earlier work this paper cites.

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)

2017

Earlier work this paper cites.

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

2017

Cited alongside, same era.

McMahan, B., et al.: Communication efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics. pp. 1273–1282 (2017)

2017

Cited alongside, same era.

Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: ICCV (2017)

2017

Cited alongside, same era.

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)

Original

2018

Cited alongside, same era.

Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: MICCAI Brainlesion Workshop. pp. 311–320 (2018)

2018

Later among the works it cites.

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: MICCAI Brainlesion Workshop. pp. 92–104 (2018)

2018

Later among the works it cites.

Yu, H., Jin, R., Yang, S.: On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization. In: ICML (2019)

2019

Closest in time.