Conference paper Open Access

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

Monica Caballero; Jon Ander Gómez; Aimilia Bantouna


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  <identifier identifierType="URL">https://zenodo.org/record/3636402</identifier>
  <creators>
    <creator>
      <creatorName>Monica Caballero</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5723-249X</nameIdentifier>
      <affiliation>everis Spain</affiliation>
    </creator>
    <creator>
      <creatorName>Jon Ander Gómez</creatorName>
      <affiliation>Universitat Politècnica de València (UPV)</affiliation>
    </creator>
    <creator>
      <creatorName>Aimilia Bantouna</creatorName>
      <affiliation>WINGS ICT Solutions</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>libraries;europe;medical services;predictive models;medical diagnostic imaging;software</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-06-05</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3636402</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/CBMS.2019.00040</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/deephealth</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The paper introduces the DeepHealth project: &amp;quot;Deep-Learning and HPC to Boost Biomedical Applications for Health&amp;quot;. This project is funded by the European Commission under the H2020 framework program and aims to reduce the gap between the availability of mature enough AI-solutions and their deployment in real scenarios. Several existing software platforms provided by industrial partners will integrate state-of-the-art machine-learning algorithms and will be used for giving support to doctors in diagnosis, increasing their capabilities and efficiency. The DeepHealth consortium is composed by 21 partners from 9 European countries including hospitals, universities, large industry and SMEs.&lt;/p&gt;

&lt;p&gt;This document is an accepted paper published using the Green Open Access Model. Published paper available at&amp;nbsp;&lt;a href="https://www.computer.org/csdl/proceedings-article/cbms/2019/228600a150/1cdNXiHj5QY"&gt;https://www.computer.org/csdl/proceedings-article/cbms/2019/228600a150/1cdNXiHj5QY&lt;/a&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;copy; 2019&amp;nbsp;IEEE.&amp;nbsp; Personal use of this material is permitted.&amp;nbsp; Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.&amp;rdquo;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825111/">825111</awardNumber>
      <awardTitle>Deep-Learning and HPC to Boost Biomedical Applications for Health</awardTitle>
    </fundingReference>
  </fundingReferences>
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