T. P. Coroller, P. Grossmann, Y. Hou, E. R. Velazquez, R. T. Leijenaar et al., CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma, Radiotherapy and Oncology, vol.114, issue.3, pp.345-350, 2015.

H. Aerts, E. R. Velazquez, R. Leijenaar, C. Parmar, P. Grossmann et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, vol.5, pp.1-8, 2014.

T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar et al., Classification of breast cancer histology images using convolutional neural networks, PloS one, vol.12, issue.6, p.177544, 2017.

J. K. Chan, The wonderful colors of the hematoxylin-eosin stain in diagnostic surgical pathology, International journal of surgical pathology, vol.22, issue.1, pp.12-32, 2014.

J. S. Meyer, C. Alvarez, C. Milikowski, N. Olson, I. Russo et al., Breast carcinoma malignancy grading by bloom-richardson system vs proliferation index: reproducibility of grade and advantages of proliferation index, Modern pathology, vol.18, issue.8, p.1067, 2005.

F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, Breast cancer histopathological image classification using convolutional neural networks, Neural Networks (IJCNN), 2016 International Joint Conference on, pp.2560-2567, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02113849

H. Cao, S. Bernard, L. Heutte, and R. Sabourin, Dissimilarity-based representation for radiomics applications, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02111139

F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, A dataset for breast cancer histopathological image classification, IEEE Transactions on Biomedical Engineering, vol.63, issue.7, pp.1455-1462, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02113843

N. A. Hamilton, R. S. Pantelic, K. Hanson, and R. D. Teasdale, Fast automated cell phenotype image classification, BMC bioinformatics, vol.8, issue.1, p.110, 2007.

L. P. Coelho, Mahotas: Open source software for scriptable computer vision, Journal of Open Research Software, vol.1, 2013.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pp.5987-5995, 2017.

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, 2017.

B. Zoph and Q. V. Le, Neural architecture search with reinforcement learning, 2016.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

G. Biau and E. Scornet, A random forest guided tour, Test, vol.25, issue.2, pp.197-227, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01221748

J. Bill and E. Fokoué, A comparative analysis of predictive learning algorithms on high-dimensional microarray cancer data, Serdica Journal of Computing, vol.8, issue.2, pp.137-168, 2014.