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
BeadNet: deep learning-based bead detection and counting in..:
Scherr, Tim
;
Streule, Karolin
;
Bartschat, Andreas
...
Bioinformatics. 36 (2020) 17 - p. 4668-4670 , 2020
Link:
https://doi.org/10.1093/bioinformatics/btaa594
RT Journal T1
BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images
UL https://suche.suub.uni-bremen.de/peid=cr-10.1093_bioinformatics_btaa594&Exemplar=1&LAN=DE A1 Scherr, Tim A1 Streule, Karolin A1 Bartschat, Andreas A1 Böhland, Moritz A1 Stegmaier, Johannes A1 Reischl, Markus A1 Orian-Rousseau, Véronique A1 Mikut, Ralf A1 Xu, Jinbo PB Oxford University Press (OUP) YR 2020 SN 1367-4803 SN 1367-4811 JF Bioinformatics VO 36 IS 17 SP 4668 OP 4670 LK http://dx.doi.org/https://doi.org/10.1093/bioinformatics/btaa594 DO https://doi.org/10.1093/bioinformatics/btaa594 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)