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

页码：E801 / E801

页数：1

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