I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Using deep learning to predict microvascular invasion in he..:
Song, Danjun
;
Wang, Yueyue
;
Wang, Wentao
...
Journal of Cancer Research and Clinical Oncology. 147 (2021) 12 - p. 3757-3767 , 2021
Link:
https://doi.org/10.1007/s00432-021-03617-3
RT Journal T1
Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters
UL https://suche.suub.uni-bremen.de/peid=cr-10.1007_s00432-021-03617-3&Exemplar=1&LAN=DE A1 Song, Danjun A1 Wang, Yueyue A1 Wang, Wentao A1 Wang, Yining A1 Cai, Jiabin A1 Zhu, Kai A1 Lv, Minzhi A1 Gao, Qiang A1 Zhou, Jian A1 Fan, Jia A1 Rao, Shengxiang A1 Wang, Manning A1 Wang, Xiaoying PB Springer Science and Business Media LLC YR 2021 SN 0171-5216 SN 1432-1335 JF Journal of Cancer Research and Clinical Oncology VO 147 IS 12 SP 3757 OP 3767 LK http://dx.doi.org/https://doi.org/10.1007/s00432-021-03617-3 DO https://doi.org/10.1007/s00432-021-03617-3 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)