Scholars@Duke publication: 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

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

Author Chunhao Wang Radiation Oncology

Author Qingrong 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

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.

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

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