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
Multimodal federated learning framework evaluation for lymp..:
, In:
2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML)
,
Ma, Ling.
;
Hu, Zhijun.
;
Yue, Ding
... - p. 269-273 , 2023
Link:
https://doi.org/10.1109/PRML59573.2023.10348287
RT T1 <cTitle>2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML)</cTitle>: T1 <Title>Multimodal federated learning framework evaluation for lymph node metastasis in gynecologic malignanciese</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10348287&Exemplar=1&LAN=DE A1 Ma, Ling. A1 Hu, Zhijun. A1 Yue, Ding A1 Wu, Guangyu A1 Shi, Xiaohua A1 Sirejiding, Shalayiding A1 Liu, Kaijiang. YR 2023 K1 Federated learning K1 Hospitals K1 Magnetic resonance imaging K1 Computational modeling K1 Predictive models K1 Sensitivity and specificity K1 Metastasis K1 Gynecologic malignancies K1 MRI K1 multi-modal learning K1 MLP K1 CNN K1 federated learning SP 269 OP 273 LK http://dx.doi.org/https://doi.org/10.1109/PRML59573.2023.10348287 DO https://doi.org/10.1109/PRML59573.2023.10348287 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
