A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation

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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. pp. 234–241. Springer International Publishing, Cham (2015)

2015

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Chen, T., Xu, B., Zhang, C., Guestrin, C.: Training deep nets with sublinear memory cost (2016), http://arxiv.org/abs/1604.06174

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2016

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Milletari, F., Navab, N., Ahmadi, S.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). pp. 565–571 (Oct 2016)

2016

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Gomez, A.N., Ren, M., Urtasun, R., Grosse, R.B.: The reversible residual network: Backpropagation without storing activations. In: Advances in Neural Information Processing Systems 30, pp. 2214–2224. Curran Associates, Inc. (2017)

2017

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Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis 36

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Blumberg, S.B., Tanno, R., Kokkinos, I., Alexander, D.C.: Deeper image quality transfer: Training low-memory neural networks for 3d images. Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018

2018

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Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net (2018), http://arxiv.org/abs/1809.10483

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2018

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Jacobsen, J.H., Smeulders, A.W., Oyallon, E.: i-revnet: Deep invertible networks. In: International Conference on Learning Representations (2018)

2018

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Myronenko, A.: 3d MRI brain tumor segmentation using autoencoder regularization (2018), http://arxiv.org/abs/1810.11654

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2018

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Wu, Y., He, K.: Group normalization (2018), http://arxiv.org/abs/1803.08494

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2018

Later among the works it cites.