Handbook of European HPC projects


Deep-Learning and HPC to Boost Biomedical Applications for Health

The main goal of the DeepHealth project is to put HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) and Computer Vision (CV) tech-niques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases. 


At the centre of the proposed innovations is the DeepHealth toolkit, an open-source free software that will provide a unified frame-work to exploit heterogeneous HPC and big data architectures assembled with DL and CV capabilities to optimise the training of predictive models. The toolkit is composed of two core libraries plus a back end offering a REST-ful API to other applications to facilitate the use of the libraries, and a dedicated front end that interacts with the back end for facilitating the use of the libraries to data scientists. The libraries are the European Distributed Deep Learning Library (EDDL) and the Euro-pean Computer Vision Library (ECVL). Version 1.0 of both libraries has been released during second semester of 2021, and both are ready to be integrated in current and new biomedical platforms or applications. 


The DeepHealth concept focuses on scenarios where image processing is needed for diagnosis. In the training environment IT experts work with datasets of images for training predictive models. In the production environment the medical personnel ingests an image coming from a scan into a platform or biomedical application that uses predictive models to get clues to support them during diagnosis. The DeepHealth toolkit will allow the IT staff to train models and run the train-ing algorithms over hybrid HPC + big data architectures without a profound knowledge of DL, CV, HPC or big data and increase their productivity reducing the required time to do it. 


The DeepHealth innovations are being validated in 14 pilots through the use of seven different biomedical and AI software plat-forms provided by partners. The libraries are being integrated and validated in seven AI and biomedical software platforms: commercial platforms: (everis Lumen, PHILIPS Open Innovation Platform, THALES PIAF) and research-oriented platforms (CEA’s ExpressIFTM, CRS4’s Digital Pathology, WINGS MigraineNet). 

The use cases cover three main areas: (i) Neurological diseases, (ii) Tumour detection and early cancer prediction and (iii) Digital pathology and automated image annotation. The pilots will allow evaluating the performance of the proposed solutions in terms of the time needed for pre-processing images, the time needed to train models and the time to put models in production. In some cases, it is expected to reduce these times from days or weeks to just hours. This is one of the major expected impacts of the DeepHealth project.

HPC and Big Data enabled Large-scale Test-beds and Applications


Coordinating Organization:
NTT Data, Spain

Project Timespan
2019-01-01 – 2022-06-30

Other Partners:
  • Azienda Ospedaliera Citta della Salute e della Scienza di Torino, Italy
  • BSC – Barcelona Supercomputing Center, Spain
  • CHUV – Centre Hospitalier Universitaire Vaudois, Switzerland
  • Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna SRL, Italy
  • CEA – Commissariat à l’Energie Atomique et aux Energies Alternatives, France
  • EPFL – Ecole Polytechnique Fédérale de Lausanne, Switzerland
  • Fundacion para el Fomento de la Investigacion Sanitaria y Biomedica de la Comunitat Valenciana, Spain
  • Karolinska Institutet, Sweden
  • Otto-von-Guericke-Universität Magdeburg, Germany
  • Philips Medical Systems Nederland BV, Netherlands
  • Pro Design Electronic GmbH, Germany
  • SIVECO Romania SA, Romania
  • Spitalul Clinic Prof Dr Theodor Burghele, Romania
  • STELAR Security Technology Law Research UG, Germany
  • Thales SIX GTS France SAS, France
  • TREE Technology SA, Spain
  • Università degli Studi di Modena e Reggio Emilia, Italy
  • Università degli Studi di Torino, Italy
  • Universitat Politècnica de València UPV, Spain
  • WINGS ICT Solutions Information & Communication Technologies IKE, Greece