, Cancer today, 2018.

N. K. Aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull et al., The European Organization for Research and Treatment of Cancer QLQ-C30: A Quality-of-Life Instrument for Use in International, JNCI: Journal of the National Cancer Institute, vol.85, pp.365-376, 1993.

J. E. Allison, I. S. Tekawa, L. J. Ransom, and A. L. Adrain, A comparison of fecal occult-blood tests for colorectal-cancer screening, New England Journal of Medicine, vol.334, 1996.

J. J. Bernal, A. Histace, M. Masana, Q. Angermann, C. Sánchez-montes et al., Polyp Detection Benchmark in Colonoscopy Videos using GTCreator: A Novel Fully Configurable Tool for Easy and Fast Annotation of Image Databases, Proceedings of 32nd CARS Conference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01846141

C. Bucilu, R. Caruana, and A. Niculescu-mizil, Model Compression, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.535-541, 2006.

J. Canny, A computational approach to edge detection, IEEE Transactions, vol.6, pp.679-698, 1986.

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Math-ers et al., Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012, International Journal of Cancer, vol.136, pp.359-386, 2015.
DOI : 10.1002/ijc.29210

URL : https://onlinelibrary.wiley.com/doi/pdf/10.1002/ijc.29210

R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol.67, pp.786-804, 1979.
DOI : 10.1109/proc.1979.11328

G. Hinton, O. Vinyals, and J. Dean, Distilling the Knowledge in a Neural Network, 2015.

P. V. Hough, Method and means for recognizing complex patterns, US Patent, vol.3, p.654, 1962.

S. Hwang, Bag Of Visual Words Approach based on SURF Features to Polyp Detection in Wireless Capsule Endoscopy Videos, vol.4

A. Karargyris and N. Bourbakis, Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos, IEEE Transactions on Biomedical Engineering, vol.58, pp.2777-2786, 2011.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012.
DOI : 10.1145/3065386

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

J. K. Lee, E. G. Liles, S. Bent, T. R. Levin, and D. A. Corley, Accuracy of fecal immunochemical tests for colorectal cancer: systematic review and meta-analysis, Annals of internal medicine, vol.160, pp.171-181, 2014.

R. D. Nawarathna, J. Oh, X. Yuan, J. Lee, and S. J. Tang, Abnormal Image Detection Using Texton Method in Wireless Capsule Endoscopy Videos, Medical Biometrics, pp.153-162, 2010.
DOI : 10.1007/978-3-642-13923-9_16

C. Orlando, P. Andrea, D. Xavier, and B. Granado, Polyps recognition using fuzzy trees, 2017 IEEE EMBS International Conference on, pp.9-12, 2017.
DOI : 10.1109/bhi.2017.7897192

URL : https://hal.archives-ouvertes.fr/hal-01896834/file/BHI2017-20161020.pdf

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.115, pp.211-252, 2015.
DOI : 10.1007/s11263-015-0816-y

URL : http://arxiv.org/pdf/1409.0575

P. Swain, Wireless capsule endoscopy. Gut, vol.52, issue.4, pp.48-50, 2003.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going Deeper With Convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.
DOI : 10.1109/cvpr.2015.7298594

URL : http://arxiv.org/pdf/1409.4842

N. Tajbakhsh, S. R. Gurudu, and J. Liang, Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information, IEEE Transactions on Medical Imaging, vol.35, pp.630-644, 2016.
DOI : 10.1109/tmi.2015.2487997

L. Von-karsa, J. Patnick, and N. Segnan, European guidelines for quality assurance in colorectal cancer screening and diagnosis. First Edition -Executive summary, Endoscopy, vol.44, issue.3, pp.1-8, 2012.

L. Yu, H. Chen, Q. Dou, J. Qin, and P. A. Heng, Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos, IEEE Journal of Biomedical and Health Informatics, vol.21, issue.1, pp.65-75, 2017.
DOI : 10.1109/jbhi.2016.2637004

Y. Yuan, W. Qin, B. Ibragimov, B. Han, L. Xing et al., Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection, In Medical Image Computing and Computer Assisted Intervention -MICCAI, vol.2018, pp.620-628, 2018.
DOI : 10.1007/978-3-030-00934-2_69

R. Zhang, Y. Zheng, C. C. Poon, D. Shen, and J. Y. Lau, Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker, Pattern Recognition, vol.83, pp.209-219, 2018.
DOI : 10.1016/j.patcog.2018.05.026

Q. Zhao, T. Dassopoulos, G. E. Mullin, M. Q. Meng, and R. Kumar, A decision fusion strategy for polyp detection in capsule endoscopy, Studies in health technology and informatics, vol.173, pp.559-565, 2012.