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INCISIVE

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INCISIVE

Project

INCISIVE

Title	A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer	Acronym	INCISIVE
Project ID	952179	Call	H2020-SC1-FA-DTS-2019-1
Programme	H2020-UE.3.1.5 - Methods and data
Activity	HTPC DT-TDS-O5-2020 Al for Health Imaging
Links
https://cordis.europa.eu/project/id/952179/it https://incisive-project.eu/

 

Abstract

Although artificial intelligence (AI) and machine learning (ML) provide unprecedented opportunities for improved cancer detection, various technical challenges as well as a lack of data availability hamper their utilisation. The EU-funded INCISIVE project aims to develop a toolbox for enhancing the accuracy, specificity and sensitivity of existing cancer imaging methods. The idea is to generate a pan-European repository of medical images that can be used for ML-based training for various types of cancer. The project's deliverables will assist the accurate prediction of tumour spread, evolution and relapse, in addition to helping stratify patients.

Partner

Partner

MAGGIOLI SPA Coordinatore INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS CENTRO REGIONALE INFORMATION E COMMUNICATION TECHNOLOGY SCARL BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION EUROPEAN DYNAMICS LUXEMBOURG SA TELESTO IOT SOLUTIONS LIMITED  INNOSYSTEMS SYMVOULEUTIKES YPIRESIES KAI EFARMOGES PLIROFORIKIS YPSILIS TECHNOLOGIAS MONOPROSOPI IDIOTIKI KEFALAIOUCHIKI ETAIREIA ARISTOTELIO PANEPISTIMIO THESSALONIKIS UNIVERZITET U NOVOM SADU MANUFACTURING TRADE AND SERVICES COMPANY,VISARIS DOO,BEOGRAD (ZEMUN) UNIVERSITA DEGLI STUDI DI NAPOLI FEDERICO II  FUNDACIO TICSALUT HELLENIC CANCER SOCIETY PAGKYPRIOS SYNDESMOS KARKINOPATHON KAI FILON 1986 UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON HELSINGIN YLIOPISTO CONSORCI INSTITUT D'INVESTIGACIONS BIOMEDIQUES AUGUST PI I SUNYER  LINAC-PET SCAN OPCO LIMITED KINGSTON UNIVERSITY HIGHER EDUCATION CORPORATION TECHNOLOGIKO PANEPISTIMIO KYPROU TIME.LEX WHITE RESEARCH SPRL MEDTRONIC IBERICA SA

 

RECIPE

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RECIPE

Project

RECIPE

Title	REliable power and time-Constrain-aware Predictive management of heterogeneous Exascale systems	Acronym	RECIPE
Project ID	801137	Call	H2020-FETHPC-2017
Programme	H2020
Activity	HTPC, cloud security, multi-cloud, distributed application, heterogeneous cloud, security SLA, decision support, deployment, monitoring, enforcement, security assurance, DevOps, lifecycle management
Links
http://www.recipe-project.eu/ https://cordis.europa.eu/project/rcn/215836_en.html

 

Abstract

The current HPC facilities will need to grow by an order of magnitude in the next few years to reach the Exascale range. The dedicated middleware needed to manage the enormous complexity of future HPC centers, where deep heterogeneity is needed to handle the wide variety of applications within reasonable power budgets, will be one of the most critical aspects in the evolution of HPC infrastructure towards Exascale. This middleware will need to address the critical issue of reliability in face of the increasing number of resources, and therefore decreasing mean time between failures. To close this gap, RECIPE provides: a hierarchical runtime resource management infrastructure optimizing energy efficiency and ensuring reliability for both time-critical and throughput-oriented computation; a predictive reliability methodology to support the enforcing of QoS guarantees in face of both transient and long-term hardware failures, including thermal, timing and reliability models; and a set of integration layers allowing the resource manager to interact with both the application and the underlying deeply heterogeneous architecture, addressing them in a disaggregate way. Quantitative goals for RECIPE include: 25% increase in energy efficiency (performance/watt) with an 15% MTTF improvement due to proactive thermal management; energy-delay product improved up to 25%; 20% reduction of faulty executions. The project will assess its results against the following set of real world use cases, addressing key application domains ranging from well established HPC applications such as geophysical exploration and meteorology, to emerging application domains such as biomedical machine learning and data analytics. To this end, RECIPE relies on a consortium composed of four leading academic partners (POLIMI,UPV,EPFL,CeRICT); two supercomputing centers, BSC and PSNC; a research hospital, CHUV, and an SME, IBTS, which provide effective exploitation avenues through industry-based use cases.

Partner

Partner

Politecnico di milano-COORDINATOR Centro Regionale Information Communication Technology scrl UNIVERSITAT POLITECNICA DE VALENCIA  BARCELLONA SUPERCOMPUTING CENTER BARCELLONA SUPERCOMPUTING CENTER POZNAN SUPERCOMPUTING AND NETWORK CENTER ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE IBT SOLUTION CENTER HOSPITALIER UNIVERSITAIRE VAUDOIS

 

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