Deep Tech INNOVATION
Open source library for Distributed Deep Learning and Tensor Operations
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Market Maturity: Exploring
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Market Creation Potential
This innovation was assessed by the JRC’s Market Creation Potential indicator framework as addressing the needs of existing markets and existing customers. Learn more
Location of Key Innovators developing this innovation
Key Innovators
UNIVERSITAT POLITECNICA DE VALENCIA
VALENCIA, ES
Higher Education Institute / Research Centre
UN Sustainable Development Goals(SDG)
This innovation contributes to the following SDG(s)
SUSTAINABLE DEVELOPMENT GOAL 3
Ensure healthy lives and promote well-being for all at all ages

The UN explains: "Significant strides have been made in increasing life expectancy and reducing some of the common killers responsible for child and maternal mortality.

Major progress has also been made on increasing access to clean water and sanitation, reducing malaria, tuberculosis, polio and the spread of HIV/AIDS.

However, many more efforts are needed to control a wide range of diseases and address many different persistent and emerging health issues."

SUSTAINABLE DEVELOPMENT GOAL 9
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

The UN explains: "Investments in infrastructure – transport, irrigation, energy and information and communication technology – are crucial to achieving sustainable development and empowering communities in many countries. It has long been recognized that growth in productivity and incomes, and improvements in health and education outcomes require investment in infrastructure."

The EU-funded Research Project
This innovation was developed under the Horizon 2020 project DeepHealth with an end date of 31/12/2021
Description of Project DeepHealth
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.

Innnovation Radar's analysis of this innovation is based on data collected on 02/11/2020.