1. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278 (1998). https://doi.org/10.1109/5.726791
2. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571
3. Christ, P.F., Ettlinger, F., Grün, F., Elshaera, M.E.A., Lipkova, J., Schlecht, S., Ahmaddy, F., Tatavarty, S., Bickel, M., Bilic, P., et al.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint. arXiv:1702.05970 (2017)
4. Postavaru, S., Stoean, R., Stoean, C., Caparros, G.J.: Adaptation of deep convolutional neural networks for cancer grading from histopathological images. In: Rojas, I., Joya, G., Catala, A. (eds.) Advances in Computational Intelligence, pp. 38–49. Springer International Publishing, Cham (2017)
5. Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Applications, pp. 169–178. Springer, Berlin (2009)