Fetching the paper…
Reading the bibliography…
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images.
French, R.M.: Catastrophic forgetting in connectionist networks. Trends in cognitive sciences 3
1999
Earlier work this paper cites.
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33
2002
Earlier work this paper cites.
Landman, B., Warfield, S.: Miccai 2012 workshop on multi-atlas labeling. In: MICCAI (2012)
2012
Earlier work this paper cites.
2016
Cited alongside, same era.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. pp. 1175–1191. ACM (2017)
2017
Cited alongside, same era.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS. pp. 1273–1282 (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
2019
Closest in time.
Roy, A.G., Conjeti, S., Navab, N., Wachinger, C.: Quicknat: A fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage 186
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…