There is a newer version of this record available.

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


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Chest X-ray</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Deep Learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Classification</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">COVID-19</subfield>
  </datafield>
  <controlfield tag="005">20201014184947.0</controlfield>
  <controlfield tag="001">3888049</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Univerisity of Turin, Computer Science dept., Torino, Italy</subfield>
    <subfield code="a">Barbano, Carlo Alberto</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Azienda Ospedaliera Citt`a della Salute e della Scienza, Presidio Molinette, Torino, Italy</subfield>
    <subfield code="a">Berzovini, Claudio</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Turin, Oncology Department, AOU San Luigi Gonzaga, Orbassano (TO), Italy</subfield>
    <subfield code="a">Calandri, Marco</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Univerisity of Turin, Computer Science dept., Torino, Italy</subfield>
    <subfield code="a">Grangetto, Marco</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">2478882</subfield>
    <subfield code="z">md5:c7890749e64b1fdc3c6a019d51269bf1</subfield>
    <subfield code="u">https://zenodo.org/record/3888049/files/covid19_TMI.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-06-10</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-covid-19</subfield>
    <subfield code="p">user-deephealth</subfield>
    <subfield code="p">user-zenodo</subfield>
    <subfield code="o">oai:zenodo.org:3888049</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Univerisity of Turin, Computer Science dept., Torino, Italy</subfield>
    <subfield code="a">Tartaglione, Enzo</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Unveiling COVID-19 from Chest X-ray with deeplearning: a hurdles race with small data</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-covid-19</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-deephealth</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-zenodo</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">825111</subfield>
    <subfield code="a">Deep-Learning and HPC to Boost Biomedical Applications for Health</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3888048</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3888049</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">preprint</subfield>
  </datafield>
</record>
875
123
views
downloads
All versions This version
Views 875207
Downloads 12370
Data volume 308.4 MB173.5 MB
Unique views 832194
Unique downloads 10759

Share

Cite as