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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
dc.contributor | Curry, Edward |
dc.contributor | Auer, Soren |
dc.contributor | Berre, Arne J. |
dc.contributor | Metzger, Andreas |
dc.contributor | Perez, Maria S. |
dc.contributor | Zillner, Sonja |
dc.contributor.author | Aldinucci, Marco |
dc.contributor.author | Atienza, David |
dc.contributor.author | Bolelli, Federico |
dc.contributor.author | Caballero, Mónica |
dc.contributor.author | Colonnelli, Iacopo |
dc.contributor.author | Quiñones, Eduardo |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2022-05-09T07:48:21Z |
dc.date.available | 2022-05-09T07:48:21Z |
dc.date.issued | 2022-04 |
dc.identifier.citation | Aldinucci, 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.isbn | 978-3-030-78307-5 |
dc.identifier.uri | http://hdl.handle.net/2117/367066 |
dc.description.abstract | At 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.sponsorship | This 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.iso | eng |
dc.publisher | Springer, Cham |
dc.rights | Attribution 3.0 Spain |
dc.rights | Attribution 4.0 International (CC BY 4.0) |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Enginyeria del software |
dc.subject.lcsh | Open source software |
dc.subject.lcsh | Deep learning (Machine learning) |
dc.subject.lcsh | Big data |
dc.subject.lcsh | Artificial intelligence |
dc.subject.other | Hybrid big data HPC architectures |
dc.subject.other | High performance data analytics |
dc.subject.other | Hardware-specific capabilities for big data GPUs FPGAs |
dc.subject.other | Performance for large-scale processing |
dc.title | 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 |
dc.type | Part of book or chapter of book |
dc.subject.lemac | Supercomputadors |
dc.identifier.doi | 10.1007/978-3-030-78307-5_9 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://doi.org/10.1007/978-3-030-78307-5_9 |
dc.rights.access | Open Access |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/825111/EU/Deep-Learning and HPC to Boost Biomedical Applications for Health/DeepHealth |
local.citation.publicationName | Technologies and Applications for Big Data Value |
local.citation.startingPage | 183 |
local.citation.endingPage | 202 |
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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