Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

@Article{Bakas2018IdentifyingTB,
 author = {S. Bakas and M. Reyes and A. Jakab and S. Bauer and M. Rempfler and A. Crimi and R. Shinohara and Christoph Berger and S. Ha and Martin Rozycki and M. Prastawa and Esther Alberts and Jana Lipková and J. Freymann and J. Kirby and M. Bilello and H. Fathallah-Shaykh and R. Wiest and J. Kirschke and Benedikt Wiestler and R. Colen and Aikaterini Kotrotsou and P. LaMontagne and D. Marcus and Mikhail Milchenko and A. Nazeri and M. Weber and A. Mahajan and U. Baid and Dongjin Kwon and Manu Agarwal and M. Alam and A. Albiol and A. Albiol and Alex Varghese and T. Tuan and T. Arbel and Aaron Avery and B. Pranjal and Subhashis Banerjee and Thomas Batchelder and N. Batmanghelich and E. Battistella and M. Bendszus and E. Benson and J. Bernal and G. Biros and M. Cabezas and Siddhartha Chandra and Yi-Ju Chang and et al.},
 booktitle = {arXiv.org},
 journal = {ArXiv},
 title = {Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge},
 volume = {abs/1811.02629},
 year = {2018}
}