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
Uncertainty aware training to improve deep learning model c..:
Dawood, Tareen
;
Chen, Chen
;
Sidhu, Baldeep S.
...
Medical Image Analysis. 88 (2023) - p. 102861 , 2023
Link:
https://doi.org/10.1016/j.media.2023.102861
RT Journal T1
Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images
UL https://suche.suub.uni-bremen.de/peid=cr-10.1016_j.media.2023.102861&Exemplar=1&LAN=DE A1 Dawood, Tareen A1 Chen, Chen A1 Sidhu, Baldeep S. A1 Ruijsink, Bram A1 Gould, Justin A1 Porter, Bradley A1 Elliott, Mark K. A1 Mehta, Vishal A1 Rinaldi, Christopher A. A1 Puyol-Antón, Esther A1 Razavi, Reza A1 King, Andrew P. PB Elsevier BV YR 2023 SN 1361-8415 JF Medical Image Analysis VO 88 SP 102861 LK http://dx.doi.org/https://doi.org/10.1016/j.media.2023.102861 DO https://doi.org/10.1016/j.media.2023.102861 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)