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Dataset Open Access

LYON19- Lymphocyte Detection Test Set

Zaneta Swiderska-Chadaj; Francesco Ciompi


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  <identifier identifierType="DOI">10.5281/zenodo.3385420</identifier>
  <creators>
    <creator>
      <creatorName>Zaneta Swiderska-Chadaj</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2115-9822</nameIdentifier>
      <affiliation>Radboudumc</affiliation>
    </creator>
    <creator>
      <creatorName>Francesco Ciompi</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8327-9606</nameIdentifier>
      <affiliation>Radboudumc</affiliation>
    </creator>
  </creators>
  <titles>
    <title>LYON19- Lymphocyte Detection Test Set</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>digital pathology</subject>
    <subject>lymphocyte detection</subject>
    <subject>immunohistochemistry</subject>
    <subject>cell detection</subject>
    <subject>ROI</subject>
    <subject>WSI</subject>
    <subject>IHC</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-09-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3385420</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3385419</relatedIdentifier>
  </relatedIdentifiers>
  <version>v1</version>
  <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;&lt;strong&gt;LYON19&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The provided test set includes 441 ROIs&amp;nbsp;saved in the .&lt;em&gt;png&lt;/em&gt;&amp;nbsp;files, and it is a test set of LYON grand challenge:&amp;nbsp;&lt;a href="https://lyon19.grand-challenge.org/"&gt;https://lyon19.grand-challenge.org&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data Description&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The test set contains&amp;nbsp;Region of Interests (ROIs) selected from&amp;nbsp;whole-slide images (WSI) of&amp;nbsp;immunohistochemistry&amp;nbsp;(IHC) stained&amp;nbsp;specimens of breast, colon and prostate.&amp;nbsp;Data came from eight different medical centers in the Netherlands. All slides were stained with an antibody against CD3 or CD8. Slides were subsequently digitized with a Pannoramic 250Flash II scanner (3DHistech, Hungary), resulting in WSIs with a spatial resolution of 0.24&amp;mu;m/px. Selected ROIs were saved with full resolution in the .&lt;em&gt;png&lt;/em&gt;&amp;nbsp;files. &amp;nbsp;&lt;/p&gt;

&lt;p&gt;Selected ROIs were representative for most different types of lymphocyte distributions that occur in slides, namely (1) area with regular lymphocyte distribution, (2) clustered cells, and (3) staining or tissue artifacts.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Citation:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Please reference the following paper if you use LYON19 data for a scientific publication:&lt;/p&gt;

&lt;p&gt;Swiderska-Chadaj, Zaneta, et al. &amp;quot;&lt;em&gt;&lt;strong&gt;Learning to detect lymphocytes in immunohistochemistry with deep learning&lt;/strong&gt;&lt;/em&gt;.&amp;quot; Medical Image Analysis (2019): 101547.&lt;/p&gt;

&lt;p&gt;Link to the paper:&amp;nbsp;&lt;a href="https://www.sciencedirect.com/science/article/pii/S1361841519300829"&gt;https://www.sciencedirect.com/science/article/pii/S1361841519300829&lt;/a&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["LYON19, https://lyon19.grand-challenge.org", "Swiderska-Chadaj, Zaneta, et al. \"Learning to detect lymphocytes in immunohistochemistry with deep learning.\" Medical Image Analysis (2019): 101547."]}</description>
  </descriptions>
</resource>
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