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
Machine Learning-based Identification of Local Recurrence R..:
Cepeda, Santiago
;
Luppino, Luigi Tommaso
;
Solheim, Ole
...
Brain and Spine. 3 (2023) - p. 101960 , 2023
Link:
https://doi.org/10.1016/j.bas.2023.101960
RT Journal T1
Machine Learning-based Identification of Local Recurrence Regions in Glioblastoma using Postoperative MRI: Implications for Survival Prognostication
UL https://suche.suub.uni-bremen.de/peid=cr-10.1016_j.bas.2023.101960&Exemplar=1&LAN=DE A1 Cepeda, Santiago A1 Luppino, Luigi Tommaso A1 Solheim, Ole A1 Pérez-Núñez, Angel A1 García-García, Sergio A1 Karlberg, Anna A1 Eikenes, Live A1 Zamora, Tomas A1 Sarabia, Rosario A1 Arrese, Ignacio A1 Kuttner, Samuel PB Elsevier BV YR 2023 SN 2772-5294 JF Brain and Spine VO 3 SP 101960 LK http://dx.doi.org/https://doi.org/10.1016/j.bas.2023.101960 DO https://doi.org/10.1016/j.bas.2023.101960 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)