1. Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., 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)
2. Chen, W., Wilson, J., Tyree, S., Weinberger, K., Chen, Y.: Compressing neural networks with the hashing trick. In: Proceedings of the International Conference on Machine Learning, pp. 2285–2294 (2015)
3. Chen, W., Sun, T., Li, M., Jiang, H., Zhou, C.: A new image co-segmentation method using saliency detection for surveillance image of coal miners. Comput. Electr. Eng. 40(8), 227–235 (2014)
4. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
5. Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.-A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage 170, 446–455 (2018)