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dc.contributorCurry, Edward
dc.contributorAuer, Soren
dc.contributorBerre, Arne J.
dc.contributorMetzger, Andreas
dc.contributorPerez, Maria S.
dc.contributorZillner, Sonja
dc.contributor.authorAldinucci, Marco
dc.contributor.authorAtienza, David
dc.contributor.authorBolelli, Federico
dc.contributor.authorCaballero, Mónica
dc.contributor.authorColonnelli, Iacopo
dc.contributor.authorQuiñones, Eduardo
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2022-05-09T07:48:21Z
dc.date.available2022-05-09T07:48:21Z
dc.date.issued2022-04
dc.identifier.citationAldinucci, M. [et al.]. The DeepHealth Toolkit: A key European free and open-source software for deep learning and computer vision ready to exploit heterogeneous HPC and cloud architectures. A: Curry, E. [et al.]. "Technologies and Applications for Big Data Value". Springer, Cham, 2022, p. 183-202.
dc.identifier.isbn978-3-030-78307-5
dc.identifier.urihttp://hdl.handle.net/2117/367066
dc.description.abstractAt the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others. i
dc.description.sponsorshipThis chapter describes work undertaken in the context of the DeepHealth project, “Deep-Learning and HPC to Boost Biomedical Applications for Health”, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111.
dc.language.isoeng
dc.publisherSpringer, Cham
dc.rightsAttribution 3.0 Spain
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Enginyeria del software
dc.subject.lcshOpen source software
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshBig data
dc.subject.lcshArtificial intelligence
dc.subject.otherHybrid big data HPC architectures
dc.subject.otherHigh performance data analytics
dc.subject.otherHardware-specific capabilities for big data GPUs FPGAs
dc.subject.otherPerformance for large-scale processing
dc.titleThe DeepHealth Toolkit: A key European free and open-source software for deep learning and computer vision ready to exploit heterogeneous HPC and cloud architectures
dc.typePart of book or chapter of book
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1007/978-3-030-78307-5_9
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-030-78307-5_9
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/825111/EU/Deep-Learning and HPC to Boost Biomedical Applications for Health/DeepHealth
local.citation.publicationNameTechnologies and Applications for Big Data Value
local.citation.startingPage183
local.citation.endingPage202
dc.description.authorship"Article signat per 19 autors/es: Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich, Jon A. Gómez, David González, Costantino Grana, Marco Grangetto, Simone Leo, Pedro López, Dana Oniga, Roberto Paredes, Luca Pireddu, Eduardo Quiñones, Tatiana Silva, Enzo Tartaglione & Marina Zapater "


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