文献
J-GLOBAL ID:201902259033117185
整理番号:19A0518088
残留畳込みネットワークを用いた低線量CT画像の改善【JST・京大機械翻訳】
Improving Low-Dose CT Image Using Residual Convolutional Network
著者 (11件):
Yang Wei
(Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China)
,
Zhang Huijuan
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Yang Jian
(Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China)
,
Wu Jiasong
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Yin Xiangrui
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Chen Yang
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Shu Huazhong
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Luo Limin
(Laboratory of Image Science and Technology, Southeast University, Nanjing, China)
,
Coatrieux Gouenou
(Institut Mines-Telecom, Telecom Bretagne, INSERM U1101 LaTIM, Brest, France)
,
Gui Zhiguo
(Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan, China)
,
Feng Qianjin
(Department of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China)
資料名:
IEEE Access
(IEEE Access)
巻:
5
ページ:
24698-24705
発行年:
2017年
JST資料番号:
W2422A
ISSN:
2169-3536
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)