Scholars@Duke
Journal cover image

Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

Publication ,  Conference
Wang, C; Li, X; Chang, Y; Sheng, Y; Zhang, J; Yin, FF; Wu, QJJ
Published in: International Journal of Radiation Oncology*Biology*Physics
September 2019
Published version (DOI) Publisher Link

Duke Scholars

Chunhao Wang
Author Chunhao Wang Radiation Oncology
Qingrong Jackie Wu
Author Qingrong Jackie Wu Radiation Oncology

Published In

International Journal of Radiation Oncology*Biology*Physics

DOI

10.1016/j.ijrobp.2019.06.760

ISSN

0360-3016

Publication Date

September 2019

Volume

105

Issue

1

Start / End Page

E789 / E790

Publisher

Elsevier BV

Related Subject Headings

  • Oncology & Carcinogenesis
  • 5105 Medical and biological physics
  • 3407 Theoretical and computational chemistry
  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis
  • 1103 Clinical Sciences
  • 0299 Other Physical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, C., Li, X., Chang, Y., Sheng, Y., Zhang, J., Yin, F. F., & Wu, Q. J. J. (2019). Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients. In International Journal of Radiation Oncology*Biology*Physics (Vol. 105, pp. E789–E790). Elsevier BV. https://doi.org/10.1016/j.ijrobp.2019.06.760
Wang, C., X. Li, Y. Chang, Y. Sheng, J. Zhang, F. F. Yin, and Q. J. J. Wu. “Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients.” In International Journal of Radiation Oncology*Biology*Physics, 105:E789–90. Elsevier BV, 2019. https://doi.org/10.1016/j.ijrobp.2019.06.760.
Wang C, Li X, Chang Y, Sheng Y, Zhang J, Yin FF, et al. Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients. In: International Journal of Radiation Oncology*Biology*Physics. Elsevier BV; 2019. p. E789–90.
Wang, C., et al. “Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients.” International Journal of Radiation Oncology*Biology*Physics, vol. 105, no. 1, Elsevier BV, 2019, pp. E789–90. Crossref, doi:10.1016/j.ijrobp.2019.06.760.
Wang C, Li X, Chang Y, Sheng Y, Zhang J, Yin FF, Wu QJJ. Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients. International Journal of Radiation Oncology*Biology*Physics. Elsevier BV; 2019. p. E789–E790.
Journal cover image

Published In

International Journal of Radiation Oncology*Biology*Physics

DOI

10.1016/j.ijrobp.2019.06.760

ISSN

0360-3016

Publication Date

September 2019

Volume

105

Issue

1

Start / End Page

E789 / E790

Publisher

Elsevier BV

Related Subject Headings

  • Oncology & Carcinogenesis
  • 5105 Medical and biological physics
  • 3407 Theoretical and computational chemistry
  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis
  • 1103 Clinical Sciences
  • 0299 Other Physical Sciences