Contribution to collective works (Parts of books)
A new Collaborative Platform for Covid-19,Benchmark Datasets
Debauche, Olivier; Mahmoudi, Saïd; Mahmoudi, Sidi Ahmed et al.
2021In Healthcare Informatics for Fighting COVID-19 and Future Epidemics
Peer reviewed
 

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Abstract :
[en] Fast and efficient collaboration among researchers is a crucial task to advance effectively in Covid-19 research. In this chapter, we present a new collaborative platform allowing to exchange and share both medical benchmark datasets and developed applications rapidly and securely between research teams. This platform aims to facilitate and encourage the exploration of new fields of research. This platform implements proven data security techniques allowing to guarantee confidentiality, mainly Argon2id password hashing algorithm, anonymization, expiration of forms, and datasets double encryption and decryption with AES 256-GCM and XChaCha20Poly1305 algorithms. Our platform has been successfully tested as part of a project aiming to develop artificial intelligence algorithms for imagery based on the detection of Covid-19. Indeed, to advance more quickly on the development of some artificial intelligence algorithms which mainly achieve both segmentation and classification of CT-scan and X-ray images of patients’ lungs and chests.
Disciplines :
Computer science
Author, co-author :
Debauche, Olivier  ;  Université de Liège - ULiège > Terra
Mahmoudi, Saïd;  Université de Mons - UMONS > IG > ILIA
Mahmoudi, Sidi Ahmed;  Université de Mons - UMONS > IG > ILIA
Manneback, Pierre;  Université de Mons - UMONS > IG > ILIA
Language :
English
Title :
A new Collaborative Platform for Covid-19,Benchmark Datasets
Publication date :
21 March 2021
Main work title :
Healthcare Informatics for Fighting COVID-19 and Future Epidemics
Publisher :
Springer
ISBN/EAN :
978-3-030-72752-9
Collection name :
EAI/Springer Innovations in Communication and Computing
Peer reviewed :
Peer reviewed
Available on ORBi :
since 26 May 2021

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