Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications / Cancilla, Michele; Canalini, Laura; Bolelli, Federico; Allegretti, Stefano; Carrión, Salvador; Paredes, Roberto; Ander Gómez, Jon; Leo, Simone; Enrico Piras, Marco; Pireddu, Luca; Badouh, Asaf; Marco-Sola, Santiago; Alvarez, Lluc; Moreto, Miquel; Grana, Costantino. - (2021), pp. 9881-9888. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milan, Italy nel Jan 10-15) [10.1109/ICPR48806.2021.9411954].

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Michele Cancilla;Laura Canalini;Federico Bolelli;Stefano Allegretti;Roberto Paredes;Costantino Grana
2021

Abstract

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.
2021
25th International Conference on Pattern Recognition, ICPR 2020
Milan, Italy
Jan 10-15
9881
9888
Cancilla, Michele; Canalini, Laura; Bolelli, Federico; Allegretti, Stefano; Carrión, Salvador; Paredes, Roberto; Ander Gómez, Jon; Leo, Simone; Enrico Piras, Marco; Pireddu, Luca; Badouh, Asaf; Marco-Sola, Santiago; Alvarez, Lluc; Moreto, Miquel; Grana, Costantino
The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications / Cancilla, Michele; Canalini, Laura; Bolelli, Federico; Allegretti, Stefano; Carrión, Salvador; Paredes, Roberto; Ander Gómez, Jon; Leo, Simone; Enrico Piras, Marco; Pireddu, Luca; Badouh, Asaf; Marco-Sola, Santiago; Alvarez, Lluc; Moreto, Miquel; Grana, Costantino. - (2021), pp. 9881-9888. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a Milan, Italy nel Jan 10-15) [10.1109/ICPR48806.2021.9411954].
File in questo prodotto:
File Dimensione Formato  
2020_ICPR_The_DeepHealth_Toolkit__A_Unified_Framework_to_Boost_Biomedical_Applications.pdf

Open access

Tipologia: Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione 1.64 MB
Formato Adobe PDF
1.64 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1212397
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 10
social impact