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 quicky. 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.

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 / Aldinucci, Marco; Atienza, David; Bolelli, Federico; Caballero, Mónica; Colonnelli, Iacopo; Flich, José; Gómez, Jon A.; González, David; Grana, Costantino; Grangetto, Marco; Leo, Simone; López, Pedro; Oniga, Dana; Paredes, Roberto; Pireddu, Luca; Quiñones, Eduardo; Silva, Tatiana; Tartaglione, Enzo; Zapater, Marina. - (2021), pp. 183-202. [10.1007/978-3-030-78307-5]

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

Federico Bolelli;Costantino Grana;
2021-01-01

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 quicky. 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.
2021
Technologies and Applications for Big Data Value
Edward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner
978-3-030-78306-8
Springer
SVIZZERA
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 / Aldinucci, Marco; Atienza, David; Bolelli, Federico; Caballero, Mónica; Colonnelli, Iacopo; Flich, José; Gómez, Jon A.; González, David; Grana, Costantino; Grangetto, Marco; Leo, Simone; López, Pedro; Oniga, Dana; Paredes, Roberto; Pireddu, Luca; Quiñones, Eduardo; Silva, Tatiana; Tartaglione, Enzo; Zapater, Marina. - (2021), pp. 183-202. [10.1007/978-3-030-78307-5]
Aldinucci, Marco; Atienza, David; Bolelli, Federico; Caballero, Mónica; Colonnelli, Iacopo; Flich, José; Gómez, Jon A.; González, David; Grana, Costantino; Grangetto, Marco; Leo, Simone; López, Pedro; Oniga, Dana; Paredes, Roberto; Pireddu, Luca; Quiñones, Eduardo; Silva, Tatiana; Tartaglione, Enzo; Zapater, Marina
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