Machine Learning Applications in Head and Neck Radiation Oncology: Lessons from Open-Source Radiomics Challenges
Hesham Elhalawani,Timothy A. Lin,Stefania Volpe,Abdallah S. R. Mohamed, Aubrey L. White, James Zafereo,Andrew J. Wong,Joel E. Berends,Shady AboHashem,Bowman Williams,Jeremy M. Aymard,Aasheesh Kanwar,Subha Perni,Crosby D. Rock,Luke Cooksey,Shauna Campbell,Pei Yang, Khahn Nguyen,Rachel B. Ger,Carlos E. Cardenas,Xenia J. Fave,Carlo Sansone,Gabriele Piantadosi,Stefano Marrone,Rongjie Liu,Chao Huang,Kaixian Yu,Tengfei Li,Yang Yu,Youyi Zhang,Hongtu Zhu,Jeffrey S. Morris,Veerabhadran Baladandayuthapani, John W. Shumway, Alakonanda Ghosh, Andrei Poehlmann,Hady A. Phoulady, Vibhas Goyal,Guadalupe Canahuate,G. Elisabeta Marai,David Vock,Stephen Y. Lai,Dennis S. Mackin,Laurence E. Court,John Freymann,Keyvan Farahani, Jayashree Kaplathy-Cramer,Clifton D. Fuller Frontiers in Oncology（2018）
Key words
machine learning,radiomics challenge,radiation oncology,head and neck,big data
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