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18. K.C.L. Wong, R.M. Summers, E. Kebebew, J. Yao, Pancreatic tumor growth prediction with
multiplicative growth and image-derived motion. Inf. Process. Med. Imaging 501–513 (2015)
19. M. Lê, H. Delingette, J. Kalpathy-Cramer, E.R. Gerstner, T. Batchelor, J. Unkelbach, N. Ayache,
Medical Image Computing and Computer-Assisted Intervention (Springer International Publishing, 2015), pp. 424–432
20. J. Friedman, T. Hastie, R. Tibshirani, Additive logistic regression: a statistical view of boosting.
Ann. Stat. 28(1), 2000 (1998)
21. K.-J. Bathe, Finite Element Method (Butterworth-Heinemann, 2000), pp. 394–409
22. A. Mohamed, C. Davatzikos, Finite element modeling of brain tumor mass-effect from 3D
medical images. Med. Image Comput. Comput. Assist. Interv. 8(Pt1), 400–408 (2005)
23. A. Hanhart, M.K. Gobbert, L.T. Izu, A memory-efficient finite element method for systems of
reaction-diffusion equations with nonsmooth forcing. J. Comput. Appl. Math. 169(2), 431–458
(2010)
24. C. Hoge, C. Davatzikos, G. Biros, An image-driven parameter estimation problem for a
reaction-diffusion glioma growth model with mass effects. J. Math. Biol. 56(6), 793–825 (2008)
25. S. Austin, An introduction to genetic algorithms. Quart. Rev. Biol. 24(4/5), 325–336 (1996)