Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C. and Buckner, R.L., 2010. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of cognitive neuroscience, 22(12), pp.2677-2684

2010

Earlier work this paper cites.

Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In NIPS pp. 1097-1105

2012

Earlier work this paper cites.

Landman, B, Warfield, S., Miccai Workshop on Multiatlas Labeling. In MICCAI Grand Challenge, 2012

2012

Earlier work this paper cites.

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In Proc. MICCAI, Springer 2015, pp. 234-241

2015

Earlier work this paper cites.

Long J, Shelhamer E, and Darrell T. Fully convolutional networks for semantic segmentation. In CVPR 2015, pp. 3431-40, IEEE

2015

Earlier work this paper cites.

Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In ICCV 2015, pp. 1520-28, IEEE

2015

Cited alongside, same era.

Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. In arXiv preprint:1511.00561, 2015

2015

Cited alongside, same era.

He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In CVPR, pp. 770-778, IEEE

2016

Cited alongside, same era.

Jimenez-del-Toro, O., et al. 2016. Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks. IEEE TMI, 35(11), pp.2459-2475

Original

2016

Cited alongside, same era.

Roy, A.G., Conjeti, S., Sheet, D., Katouzian, A., Navab, N. and Wachinger, C., 2017, September. Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data. In MICCAI, pp. 231-239, Springer

2017

Later among the works it cites.

Jégou, S., Drozdzal, M., Vazquez, D., Romero, A. and Bengio, Y., 2017, July. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In CVPR Workshop, pp. 1175-1183, IEEE

2017

Later among the works it cites.

Roy, A.G., Conjeti, S., Navab, N. and Wachinger, C., 2018. QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds. ArXiv:1801.04161

Original

2018

Closest in time.

Hu, J., Shen, L. and Sun, G. Squeeze-and-excitation networks. In CVPR 2018

2018

Closest in time.