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"Identifying the Best Machine Learning Algorithms for Brain Tumor ..."

Spyridon Bakas et al. (2018)

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DOI: —

access: open

type: Informal or Other Publication

metadata version: 2021-10-14

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  - electronic edition @ arxiv.org (open access)
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  dblp key:
  - journals/corr/abs-1811-02629
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  persistent URL:
  - https://dblp.org/rec/journals/corr/abs-1811-02629
Spyridon Bakas, Mauricio Reyes, András Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipková, John B. Freymann, Justin S. Kirby, Michel Bilello, Hassan M. Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka R. Colen, Aikaterini Kotrotsou, Pamela LaMontagne, Daniel S. Marcus, Mikhail Milchenko, Arash Nazeri, Marc-André Weber, Abhishek Mahajan, Ujjwal Baid, Dongjin Kwon, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Alex Varghese, Tran Anh Tuan, Tal Arbel, Aaron Avery, Pranjal B., Subhashis Banerjee, Thomas Batchelder, Kayhan N. Batmanghelich, Enzo Battistella, Martin Bendszus, Eze Benson, José Bernal, George Biros, Mariano Cabezas, Siddhartha Chandra, Yi-Ju Chang, et al.:
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. CoRR abs/1811.02629 (2018)

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