Interpretable Feature-Specific Generative Adversarial Network Model for 3D CBCT Image Enhancement for Radiomics

被引：0
|
作者：
Qiao, Z. [1 ]
Zhang, Z. [2 ]
Jiang, Z. [2 ]
Lai, Y. [3 ]
Lee, J. [4 ]
Wu, D. [1 ]
Beltran, C. [5 ]
Ren, L. [6 ]
Huang, M. [7 ]
机构：
[1] Univ Florida, Gainesville, FL USA
[2] Duke Univ, Durham, NC USA
[3] Univ Texas Arlington, Arlington, TX USA
[4] Duke Radiat Oncol, Durham, NC USA
[5] Mayo Clin, Jacksonville, FL 32224 USA
[6] Univ Maryland, Baltimore, MD 21201 USA
[7] Mayo Clin Florida, Jacksonville, FL USA
来源：
MEDICAL PHYSICS | 2022年 / 49卷 / 06期
关键词：
D O I：
暂无
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
PO-GePV-M-
引用
收藏
页码：E801 / E801
页数：1
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