V. Badrinarayanan, A. Kendall, and R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, 2015.

B. E. Bejnordi, the CAMELYON16 consortium: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer, JAMA, vol.318, issue.22, pp.2199-2210, 2017.

K. Fukushima, Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position-Neocognitron, ELECTRON. & COMMUN. JAPAN, vol.62, issue.10, pp.11-18, 1979.

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.

O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, pp.234-241, 2015.

A. Rosenfeld, Digital topology, Amer. Math. Monthly, vol.86, pp.621-630, 1979.

L. Vincent, Morphological gray scale reconstruction in image analysis: Applications and efficient algorithms, IEEE Transactions on Image Processing, vol.2, issue.2, pp.176-201, 1993.

D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, Deep Learning for Identifying Metastatic Breast Cancer, 2016.

M. D. Zeiler, ADADELTA: An Adaptive Learning Rate Method, 2012.