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2021-06-22 - Livre/Chapitre ou partie - Anglais - 17 page(s)

Debauche Olivier , Mahmoudi Said , Mahmoudi Sidi , Manneback Pierre , "A new Collaborative Platform for Covid-19, Benchmark Datasets" in "Intelligent Healthcare Informatics for Fighting the COVID-19 and Other Pandemics and Epidemics"

  • Edition : Springer
  • Codes CREF : Sciences agronomiques (DI3600), Informatique générale (DI1162)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) Fast and efficient collaboration between researcher 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. The aim of this platform is 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 upon detection of Covid-19. Indeed, by using our collaborative platform allowed us 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.


Mots-clés :
  • (Anglais) medical research
  • (Anglais) sharing data
  • (Anglais) exchange data
  • (Anglais) Covid-19
  • (Anglais) medical data