1. Artan, Y., Haider, M.A., Langer, D.L., van der Kwast, T.H., Evans, A.J., Yang, Y., Wernick, M.N., Trachtenberg, J., Yetik, I.S.: Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. TIP 19(9), 2444–2455 (2010)
2. Fehr, D., Veeraraghavan, H., Wibmer, A., Gondo, T., Matsumoto, K., Vargas, H.A., Sala, E., Hricak, H., Deasy, J.O.: Automatic classification of prostate cancer gleason scores from multiparametric magnetic resonance images. Proc. NAS 112(46), E6265–E6273 (2015)
3. Lemaitre, G.: Computer-Aided Diagnosis for Prostate Cancer using Multi-Parametric Magnetic Resonance Imaging. Ph.D. thesis, Universite de Bourgogne; Universitat de Girona (2016)
4. Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in MRI. TMI 33(5), 1083–1092 (2014)
5. Niaf, E., Flamary, R., Rouviere, O., Lartizien, C., Canu, S.: Kernel-based learning from both qualitative and quantitative labels: application to prostate cancer diagnosis based on multiparametric MR imaging. TIP 23(3), 979–991 (2014)