Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lac...

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Detalles Bibliográficos
Autores principales: Elhalawani, Hesham, Lin, Timothy A., Volpe, Stefania, Mohamed, Abdallah S. R., White, Aubrey L., Zafereo, James, Wong, Andrew J., Berends, Joel E., AboHashem, Shady, Williams, Bowman, Aymard, Jeremy M., Kanwar, Aasheesh, Perni, Subha, Rock, Crosby D., Cooksey, Luke, Campbell, Shauna, Yang, Pei, Nguyen, Khahn, Ger, Rachel B., Cardenas, Carlos E., Fave, Xenia J., Sansone, Carlo, Piantadosi, Gabriele, Marrone, Stefano, Liu, Rongjie, Huang, Chao, Yu, Kaixian, Li, Tengfei, Yu, Yang, Zhang, Youyi, Zhu, Hongtu, Morris, Jeffrey S., Baladandayuthapani, Veerabhadran, Shumway, John W., Ghosh, Alakonanda, Pöhlmann, Andrei, Phoulady, Hady A., Goyal, Vibhas, Canahuate, Guadalupe, Marai, G. Elisabeta, Vock, David, Lai, Stephen Y., Mackin, Dennis S., Court, Laurence E., Freymann, John, Farahani, Keyvan, Kaplathy-Cramer, Jayashree, Fuller, Clifton D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6107800/
https://www.ncbi.nlm.nih.gov/pubmed/30175071
http://dx.doi.org/10.3389/fonc.2018.00294

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