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 Approach on Segmentation of AgNOR-Stained Cytology ..:
, In:
2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
,
Atkinson Amorim, Joao Gustavo
;
Buschetto Macarini, Luiz Antonio
;
Victoria Matias, Andre
... - p. 552-557 , 2020
Link:
https://doi.org/10.1109/CBMS49503.2020.00110
RT T1 <cTitle>2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)</cTitle>: T1 <Title>A Novel Approach on Segmentation of AgNOR-Stained Cytology Images Using Deep Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9183262&Exemplar=1&LAN=DE A1 Atkinson Amorim, Joao Gustavo A1 Buschetto Macarini, Luiz Antonio A1 Victoria Matias, Andre A1 Cerentini, Allan A1 Botelho De Miranda Onofre, Fabiana A1 Sherlley Casimiro Onofre, Alexandre A1 Von Wangenheim, Aldo YR 2020 SN 2372-9198 K1 Cancer K1 Lesions K1 Training K1 Image segmentation K1 Machine learning K1 Satellites K1 Software K1 AgNOR K1 Cytology K1 Deep Learning K1 Nucleolar Organizer Regions SP 552 OP 557 LK http://dx.doi.org/https://doi.org/10.1109/CBMS49503.2020.00110 DO https://doi.org/10.1109/CBMS49503.2020.00110 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
