Screening for Prostate Cancer: A Review of the Evidence for the U.S. Preventive Services Task Force, Annals of Internal Medicine, vol.155, issue.11, pp.762-771, 2011. ,
DOI : 10.7326/0003-4819-155-11-201112060-00375
ETS Fusion Genes in Prostate Cancer, Prostate Cancer, ser. Protein Reviews, vol.16, pp.139-183 ,
DOI : 10.1007/978-1-4614-6828-8_5
The role of MRI in active surveillance of prostate cancer, Current Opinion in Urology, vol.23, issue.3, pp.261-267, 2013. ,
DOI : 10.1097/MOU.0b013e32835f899f
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review, Computers in Biology and Medicine, vol.60, pp.8-31, 2015. ,
DOI : 10.1016/j.compbiomed.2015.02.009
URL : https://hal.archives-ouvertes.fr/hal-01235868
Computer-Aided Diagnosis for Prostate Cancer using Multi-Parametric Magnetic Resonance Imaging, 2016. ,
URL : https://hal.archives-ouvertes.fr/tel-01411957
Normalization of t2w-mri prostate images using rician a priori, SPIE Medical Imaging. International Society for Optics and Photonics, pp.978-529, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01265774
New variants of a method of MRI scale standardization, IEEE Transactions on Medical Imaging, vol.19, issue.2, pp.143-150, 2000. ,
DOI : 10.1109/42.836373
Classification of prostate magnetic resonance spectra using Support Vector Machine, Biomedical Signal Processing and Control, vol.7, issue.5, pp.499-508, 2012. ,
DOI : 10.1016/j.bspc.2011.09.003
URL : https://hal.archives-ouvertes.fr/hal-00650862
Image features from phase congruency, Videre: Journal of computer vision research, vol.1, issue.3, pp.1-26, 1999. ,
Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973. ,
DOI : 10.1109/TSMC.1973.4309314
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.434.6
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002. ,
DOI : 10.1109/TPAMI.2002.1017623
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.1576
Accurate estimation of pharmacokinetic contrast-enhanced dynamic MRI parameters of the prostate, Journal of Magnetic Resonance Imaging, vol.211, issue.4, pp.607-614, 2001. ,
DOI : 10.1002/jmri.1085
Pharmacokinetic Parameters in CNS Gd-DTPA Enhanced MR Imaging, Journal of Computer Assisted Tomography, vol.15, issue.4, pp.621-628, 1991. ,
DOI : 10.1097/00004728-199107000-00018
Pharmacokinetic Mapping of the Breast: A New Method for Dynamic MR Mammography, Magnetic Resonance in Medicine, vol.12, issue.4, pp.506-514, 1995. ,
DOI : 10.1002/mrm.1910330408
Quantitative Analysis of Dynamic Gd-DTPA Enhancement in Breast Tumors Using a Permeability Model, Magnetic Resonance in Medicine, vol.26, issue.4, pp.564-568, 1995. ,
DOI : 10.1002/mrm.1910330416
A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging, Computerized Medical Imaging and Graphics, vol.46, pp.219-226, 2015. ,
DOI : 10.1016/j.compmedimag.2015.09.001
An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization, Journal of Magnetic Resonance, vol.158, issue.1-2, pp.164-168, 2002. ,
DOI : 10.1016/S1090-7807(02)00069-1
Computer-Aided Detection of Prostate Cancer in MRI, IEEE Transactions on Medical Imaging, vol.33, issue.5, pp.1083-1092, 2014. ,
DOI : 10.1109/TMI.2014.2303821
knn approach to unbalanced data distributions: a case study involving information extraction, Proceedings of Workshop on Learning from Imbalanced Datasets, 2003. ,
An instance level analysis of data complexity, Machine Learning, vol.8, issue.7, pp.225-256, 2014. ,
DOI : 10.1007/s10994-013-5422-z
Smote: synthetic minority over-sampling technique, Journal of artificial intelligence research, pp.321-357, 2002. ,
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, International Conference on Intelligent Computing, pp.878-887, 2005. ,
DOI : 10.1007/11538059_91
Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, Journal of Machine Learning Research, vol.18, issue.17, pp.1-516, 2017. ,
Scikit-learn: Machine learning in python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992. ,
DOI : 10.1016/S0893-6080(05)80023-1
Applied logistic regression, 2004. ,
DOI : 10.1002/9781118548387