DSpace
 

Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Publications Internationales >

Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11182

Titre: Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Auteur(s): Sendra-Balcells, Carla
Campello, Víctor M.
Torrents-Barrena, Jordina
Ammar, Mohammed
Date de publication: 2023
Editeur: Nature Research
Collection/Numéro: Scientific Reports/ Vol.13, N°1 (2023);p.1
Résumé: The Funding section in the original version of this Article was incomplete. “This work received funding from the European Union’s 2020 research and innovation programme under Grant Agreement No. 825903 (euCanSHare project), as well as from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. Additionally, the research leading to these results has received funding from Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK).” now reads: “This work received funding from the European Union’s 2020 research and innovation programme under Grant Agreement No. 825903 (euCanSHare project), as well as from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. Additionally, the research leading to these results has received funding from Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK). This research was partly funded by a grant from the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (AIMIX project - grant agreement No. 101044779).”
URI/URL: https://doi.org/10.1038/s41598-023-29490-3
https://www.nature.com/articles/s41598-023-30540-z
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11182
ISSN: 20452322
Collection(s) :Publications Internationales

Fichier(s) constituant ce document :

Fichier Description TailleFormat
Mohammed Ammar.pdf740,01 kBAdobe PDFVoir/Ouvrir
View Statistics

Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.

 

Valid XHTML 1.0! Ce site utilise l'application DSpace, Version 1.4.1 - Commentaires