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
A Novel Deep Learning Method for Nuclear Cataract Classific..:
, In:
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
,
Zhang, Xiaoqing
;
Xiao, Zunjie
;
Higashita, Risa
... - p. 662-668 , 2020
Link:
https://doi.org/10.1109/SMC42975.2020.9283218
RT T1
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
: T1
A Novel Deep Learning Method for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images
UL https://suche.suub.uni-bremen.de/peid=ieee-9283218&Exemplar=1&LAN=DE A1 Zhang, Xiaoqing A1 Xiao, Zunjie A1 Higashita, Risa A1 Chen, Wan A1 Yuan, Jin A1 Fang, Jiansheng A1 Hu, Yan A1 Liu, Jiang YR 2020 SN 2577-1655 K1 Cataracts K1 Optical losses K1 Image segmentation K1 Solid modeling K1 Optical coherence tomography K1 Lenses K1 Diseases K1 AS-OCT image K1 nuclear cataract K1 GraNet K1 cross-training method SP 662 OP 668 LK http://dx.doi.org/https://doi.org/10.1109/SMC42975.2020.9283218 DO https://doi.org/10.1109/SMC42975.2020.9283218 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)