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Deep-Learning and HPC to Boost Biomedical Applications for Health

Project description

DEENESFRITPL

Supercomputing and Big Data for biomedical applications

The so-called fourth paradigm of science is based on the unified environments of high-performance computing (HPC) and Big Data analytics. It is expected to considerably advance health scientific research and innovation. The EU-funded DeepHealth project will deliver HPC power at the service of biomedical applications and apply deep learning (DL) techniques on vast and compound biomedical data sets, aiming to underpin new and more effective methods of diagnosis, monitoring and treatment of diseases. The project will develop a resilient and scalable structure for the HPC + Big Data environment that will rely on two new libraries: the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL). The structure, after it is validated, will allow training of models and provide training data from different medical fields.

Objective

Health scientific discovery and innovation are expected to quickly move forward under the so called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments.
Under this paradigm, the DeepHealth project will provide HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) techniques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases.
DeepHealth will develop a flexible and scalable framework for the HPC + Big Data environment, based on two new libraries: the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL). The framework will be validated in 14 use cases which will allow to train models and provide training data from different medical areas (migraine, dementia, depression, etc.). The resulting trained models, and the libraries, will be integrated and validated in 7 existing biomedical software platforms, which include: a) commercial platforms (e.g. PHILIPS Clinical Decision Support System from or THALES SIX PIAF; and b) research oriented platforms (e.g. CEA`s ExpressIF™ or CRS4`s Digital Pathology). Impact is measured by tracking the time-to-model-in-production (ttmip).
Through this approach, DeepHealth will also standardise HPC resources to the needs of DL applications, and underpin the compatibility and uniformity on the set of tools used by medical staff and expert users. The final DeepHealth solution will be compatible with HPC infrastructures ranging from the ones in supercomputing centers to the ones in hospitals.
DeepHealth involves 21 partners from 9 European Countries, gathering a multidisciplinary group from research organisations (9), health organisations (4) as well as (4) large and (4) SME industrial partners, with strong commitment towards innovation, exploitation and sustainability.

Fields of science

Programme(s)

Topic(s)

Call for proposal

H2020-ICT-2018-20

See other projects for this call

Sub call

H2020-ICT-2018-2

Funding Scheme

IA - Innovation action

Coordinator

NTT DATA SPAIN, SL
Net EU contribution
€ 314 980,14
Address
Camino fuente de la mora 1
28050 Madrid
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Other funding
€ 134 991,49

Participants (24)

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EC signature date 5 December 2018
Last update: 16 December 2022

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Permalink: https://cordis.europa.eu/project/id/825111

European Union, 2023