1. Brosch, T., Tang, L.Y.W., Yoo, Y., Li, D.K.B., Traboulsee, A., Tam, R.: Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016).
https://doi.org/10.1109/TMI.2016.2528821
2. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. CoRR abs/1606.06650 (2016).
http://arxiv.org/abs/1606.06650
3. Datta, S., Sajja, B.R., He, R., Gupta, R.K., Wolinsky, J.S., Narayana, P.A.: Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 25(5), 932–937 (2007)
4. Lecture Notes in Computer Science;GM Fleishman,2018
5. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015).
http://arxiv.org/abs/1502.03167