13M€ EU H2020 DeepHealth project is heading to kick-off

DeepHealth:  Deep-Learning and HPC to Boost Biomedical Applications for Health

EU H2020 ICT-11-2018 Innovation Action – Total cost 12.8M€, 2019-2021 (36 months)

The Department of Computer Science of the University of Turin, together with the departments of neuroscience and medical sciences of the University of Turin, the consortium of public hospitals in the city of Turin (city of science and health) and 20 other European academic and industrial partners they are joining forces to push the methods of Artificial Intelligence, supported by High-Performance Computing, towards superhuman precision levels to support diagnosis, monitoring and cure of diseases.

As parallel computing reserach group, we will support deephealth with a novel platform for the training of deep networks with medical images. The platform will be novel, high-performance, easy to use, fully open and available for use to the whole national reserach community by way of the HPC cloud ecosystem at  HPC4AI (Competence Center for High Performance and Artificial Intelligence Turin).

Excited.


DeepHealth:  Deep-Learning and HPC to Boost Biomedical Applications for Health

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 PIAF; and b) research-oriented platforms (e.g. CEA`s ExpressIFTM or CRS4`s Digital Pathology). The 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 centres 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 a strong commitment towards innovation, exploitation and sustainability.

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About Marco Aldinucci

Marco Aldinucci is an assistant professor at Computer Science Department of the University of Torino since 2008. Previously, he has been researcher at University of Pisa and Italian National Research Agency. He is the author of over a hundred papers in international journals and conference proceeding (Google scholar h-index 21). He has been participating in over 20 national and international research projects concerning parallel and autonomic computing. He is the recipient of the HPC Advisory Council University Award 2011 and the NVidia Research award 2013. He has been leading the “Low-Level Virtualization and Platform-Specific Deployment” workpackage within the EU-STREP FP7 ParaPhrase (Parallel Patterns for Adaptive Heterogeneous Multicore Systems) project, the GPGPU workpackage within the IMPACT project (Innovative Methods for Particle Colliders at the Terascale), and he is the contact person for University of Torino for the European Network of Excellence on High Performance and Embedded Architecture and Compilation. In the last year he delivered 5 invited talks in international workshops (March 2012 – March 2013). He co-designed, together with Massimo Torquati, the FastFlow programming framework and several other programming frameworks and libraries for parallel computing. His research is focused on parallel and distributed computing.