Project description
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
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- medical and health sciencesbasic medicineneurologydementia
- medical and health sciencesbasic medicinepathology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Keywords
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
28050 Madrid
Spain
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Participants (24)
Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
50002 Zaragoza
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46022 Valencia
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5684 PC Best
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Participation ended
013685 BUCURESTI
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171 21 Nea Smyrni
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
92230 Gennevilliers
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75015 PARIS 15
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08034 Barcelona
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83052 Bruckmuehl
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
10124 Torino
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
00185 Roma
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41121 Modena
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09050 Pula Cagliari
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17177 Stockholm
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46010 Valencia
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10126 Torino
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39106 Magdeburg
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20457 HAMBURG
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050659 Bucaresti
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1015 Lausanne
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1011 Lausanne
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33003 Oviedo
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
104 22 Stockholm
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013685 Bucuresti
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.