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Preprint Open Access

Unveiling COVID-19 from Chest X-ray with deeplearning: a hurdles race with small data

Tartaglione, Enzo; Barbano, Carlo Alberto; Berzovini, Claudio; Calandri, Marco; Grangetto, Marco


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  <identifier identifierType="DOI">10.5281/zenodo.3888049</identifier>
  <creators>
    <creator>
      <creatorName>Tartaglione, Enzo</creatorName>
      <givenName>Enzo</givenName>
      <familyName>Tartaglione</familyName>
      <affiliation>Univerisity of Turin, Computer Science dept., Torino, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Barbano, Carlo Alberto</creatorName>
      <givenName>Carlo Alberto</givenName>
      <familyName>Barbano</familyName>
      <affiliation>Univerisity of Turin, Computer Science dept., Torino, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Berzovini, Claudio</creatorName>
      <givenName>Claudio</givenName>
      <familyName>Berzovini</familyName>
      <affiliation>Azienda Ospedaliera Citt`a della Salute e della Scienza, Presidio Molinette, Torino, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Calandri, Marco</creatorName>
      <givenName>Marco</givenName>
      <familyName>Calandri</familyName>
      <affiliation>University of Turin, Oncology Department, AOU San Luigi Gonzaga, Orbassano (TO), Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Grangetto, Marco</creatorName>
      <givenName>Marco</givenName>
      <familyName>Grangetto</familyName>
      <affiliation>Univerisity of Turin, Computer Science dept., Torino, Italy</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Unveiling COVID-19 from Chest X-ray with deeplearning: a hurdles race with small data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Chest X-ray</subject>
    <subject>Deep Learning</subject>
    <subject>Classification</subject>
    <subject>COVID-19</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-06-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3888049</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3888048</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/covid-19</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/deephealth</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The possibility to use widespread and simple chestX-ray (CXR) imaging for early screening of COVID-19 patientsis attracting much interest from both the clinical and the AIcommunity. In this study we provide insights and also raisewarnings on what is reasonable to expect by applying deeplearning to COVID classification of CXR images. We providea methodological guide and critical reading of an extensive set ofstatistical results that can be obtained using currently availabledatasets. In particular, we take the challenge posed by currentsmall size COVID data and show how significant can be thebias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing resultson a medium size COVID CXR dataset, just collected by oneof the major emergency hospitals in Northern Italy during thepeak of the COVID pandemic. These novel data allow us tocontribute to validate the generalization capacity of preliminaryresults circulating in the scientific community. Our conclusionsshed some light into the possibility to effectively discriminateCOVID using CXR.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</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>
</resource>
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