January 30, 2018 Dataset Open Access

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

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Sara Moccia</dc:creator>
  <dc:creator>Gabriele Omodeo Vanone</dc:creator>
  <dc:creator>Elena De Momi</dc:creator>
  <dc:creator>Leonardo S. Mattos</dc:creator>
  <dc:date>2018-01-30</dc:date>
  <dc:description>The NBI-InfFrames dataset aims to provide the surgical data science community with a labeled dataset for the identification of informative endoscopic video frames. 

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

The frames include 180 informative (I) video frames, 180 blurred (B) frames, 180 frames with saliva or specular reflections (S) and 180 underexposed (U) frames.

The dataset was created for testing the method proposed in S. Moccia, et al. "Learning-based classification of informative laryngoscopic frames." COMPUTER METHODS AND PROGRAM IN BIOMEDICINE, (accepted for publication).

The folder FRAMES.zip contains 3 subfolders (FOLD1, FOLD2, FOLD3), which are the 3 folds used for cross-validation purpose in the frame classification performance assessment. Data separation in the folds is performed both at image- and patient-level.

Each subfolder contains 4 folders relative to the four frame classes, i.e., I, B, S and U.</dc:description>
  <dc:identifier>https://zenodo.org/record/1162784</dc:identifier>
  <dc:identifier>10.5281/zenodo.1162784</dc:identifier>
  <dc:identifier>oai:zenodo.org:1162784</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.1162783</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/legalcode</dc:rights>
  <dc:subject>Frame selection, NBI endoscopy, machine learning, classification</dc:subject>
  <dc:title>NBI-InfFrames</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>