January 30, 2018 Dataset Open Access

Sara Moccia; Gabriele Omodeo Vanone; Elena De Momi; Leonardo S. Mattos

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  <identifier identifierType="DOI">10.5281/zenodo.1162784</identifier>
  <creators>
    <creator>
      <creatorName>Sara Moccia</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4494-8907</nameIdentifier>
      <affiliation>Istituto Italiano di Tecnologia / Politecnico di Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Gabriele Omodeo Vanone</creatorName>
      <affiliation>Politecnico di Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Elena De Momi</creatorName>
      <affiliation>Politecnico di Milano</affiliation>
    </creator>
    <creator>
      <creatorName>Leonardo S. Mattos</creatorName>
      <affiliation>Istituto Italiano di Tecnologia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>NBI-InfFrames</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Frame selection, NBI endoscopy, machine learning, classification</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-01-30</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1162784</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1162783</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc/4.0/legalcode">Creative Commons Attribution Non Commercial 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The&amp;nbsp;&lt;strong&gt;NBI-InfFrames &lt;/strong&gt;dataset&lt;strong&gt;&amp;nbsp;&lt;/strong&gt;aims to provide the surgical data science&amp;nbsp;community with a&amp;nbsp;labeled dataset for the identification of informative endoscopic video&amp;nbsp;frames.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;It&amp;nbsp;is&amp;nbsp;composed&amp;nbsp;of 720&amp;nbsp;video frames. The frames are manually&amp;nbsp;extracted and labeled from 18 narrow-band laryngoscopic videos of 18 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The frames include 180 informative (&lt;strong&gt;I&lt;/strong&gt;) video frames, 180 blurred (&lt;strong&gt;B&lt;/strong&gt;)&amp;nbsp;frames, 180 frames with saliva or specular reflections (&lt;strong&gt;S&lt;/strong&gt;) and 180 underexposed (&lt;strong&gt;U&lt;/strong&gt;) frames.&lt;/p&gt;

&lt;p&gt;The dataset was created for testing the method proposed in S. Moccia, et al. &amp;quot;&lt;em&gt;Learning-based classification of informative laryngoscopic frames.&lt;/em&gt;&amp;quot; COMPUTER METHODS AND PROGRAM IN BIOMEDICINE, (accepted for publication).&lt;/p&gt;

&lt;p&gt;The folder&lt;em&gt;&amp;nbsp;&lt;strong&gt;FRAMES.zip&lt;/strong&gt;&amp;nbsp;&lt;/em&gt;contains 3 subfolders (FOLD1, FOLD2, FOLD3), which are the 3 folds used for cross-validation purpose in the frame&amp;nbsp;classification performance assessment. Data separation in the folds is performed both at image- and patient-level.&lt;/p&gt;

&lt;p&gt;Each subfolder contains 4 folders relative to the four frame classes, i.e., &lt;strong&gt;I&lt;/strong&gt;, &lt;strong&gt;B&lt;/strong&gt;, &lt;strong&gt;S&lt;/strong&gt; and &lt;strong&gt;U&lt;/strong&gt;.&lt;/p&gt;</description>
  </descriptions>
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