{"id":14980,"date":"2025-07-03T18:19:47","date_gmt":"2025-07-03T16:19:47","guid":{"rendered":"https:\/\/www.crs4.it\/projects\/deephealth\/"},"modified":"2026-04-02T12:33:40","modified_gmt":"2026-04-02T10:33:40","slug":"deephealth","status":"publish","type":"projects","link":"https:\/\/www.crs4.it\/en\/projects\/deephealth\/","title":{"rendered":"DeepHealth"},"content":{"rendered":"<p>Health scientific discovery and innovation are expected to quickly  move forward under the so-called \u201cfourth paradigm of science\u201d, which  relies on unifying the traditionally separated and heterogeneous  high-performance computing and big data analytics environments.<br \/> 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.<br \/>\nDeepHealth 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).<br \/>\nThrough  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.<br \/> 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.<\/p>\n","protected":false},"featured_media":11084,"template":"","meta":{"_acf_changed":false},"gruppo":[123],"class_list":["post-14980","projects","type-projects","status-publish","has-post-thumbnail","hentry","gruppo-data-intensive-computing-en"],"acf":{"title":"Deep-Learning and HPC to Boost Biomedical Applications for Health","acronym":"DeepHealth","project_uuid":"c5c0f0a7-c584-11e8-9d48-0030485a3849","start_date":"01\/12\/2018","end_date":"31\/05\/2022","funder":"EU H2020","partners":"\n<table style=\"width:100%; border-collapse: collapse;\">\n  <tr>\n    <th style=\"border: 1px solid #ddd; padding: 8px; background-color: #f2f2f2;\">Nome<\/th>\n    <th style=\"border: 1px solid #ddd; padding: 8px; background-color: #f2f2f2;\">Nazione<\/th>\n    <th style=\"border: 1px solid #ddd; padding: 8px; background-color: #f2f2f2;\">Ruolo<\/th>\n  <\/tr>\n\n  <tr>\n    <td style=\"border: 1px solid #ddd; padding: 8px;\"><a href=\"https:\/\/www.everis.com\/global\/en\" target=\"_blank\">EVERIS SPAIN SL<\/a><\/td>\n    <td style=\"border: 1px solid #ddd; padding: 8px;\">Spain<\/td>\n    <td style=\"border: 1px solid #ddd; padding: 8px;\">Coordinator<\/td>\n  <\/tr>\n<\/table>","team_members":[],"proj_website":"https:\/\/deephealth-project.eu\/","project_status":"Completed","project_coordinator":[{"ID":10066,"post_author":"4","post_date":"2025-06-30 17:25:33","post_date_gmt":"2025-06-30 15:25:33","post_content":"Luca Pireddu earned his degree in Computing Science in 2003 from Laurentian University in Sudbury, Canada. Here he also started his research work joining MIRARCO in 2002, where he completed his undergraduate thesis on computational methods to optimize underground mine plans. He later pursued graduate studies at the University of Alberta, Canada, receiving his M.Sc. in Computing Science in 2006 with a thesis on predicting biological pathways by proteome analysis - a project that he continued as a researcher at U. of A. after his studies. After a stint in the private sector, Luca joined CRS4, where he currently works with the Visual and Data-Intensive Computing group. He has coordinated CRS4's participation in several EU, national and regional research projects in the area of novel development and application of data processing, analysis and management techniques to concrete problems in several fields of interest - particularly, bioinformatics, biomedical research and urban computing. He is particularly interested in techniques for facilitating scalability of complex computational scientific workflows, secure and scalable methods to leverage sensitive data collections, and data and workflow re-use through the application of FAIR principles. He represents CRS4 at the Italian nodes of the BBMRI-ERIC and ELIXIR EU Research Infrastructures and at the Data, AI and Robotics (DAIRO) Association (ex BDVA).","post_title":"Luca Pireddu","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"luca-pireddu","to_ping":"","pinged":"","post_modified":"2026-04-06 20:13:15","post_modified_gmt":"2026-04-06 18:13:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.crs4.it\/people\/luca-pireddu\/","menu_order":0,"post_type":"people","post_mime_type":"","comment_count":"0","filter":"raw"}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>DeepHealth - CRS4<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.crs4.it\/en\/projects\/deephealth\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DeepHealth - CRS4\" \/>\n<meta property=\"og:description\" content=\"Health scientific discovery and innovation are expected to quickly move forward under the so-called \u201cfourth paradigm of science\u201d, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. 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