January 30, 2018 Dataset Open Access

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

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{
  "description": "<p>The&nbsp;<strong>NBI-InfFrames </strong>dataset<strong>&nbsp;</strong>aims to provide the surgical data science&nbsp;community with a&nbsp;labeled dataset for the identification of informative endoscopic video&nbsp;frames.&nbsp;</p>\n\n<p>It&nbsp;is&nbsp;composed&nbsp;of 720&nbsp;video frames. The frames are manually&nbsp;extracted and labeled from 18 narrow-band laryngoscopic videos of 18 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination).&nbsp;</p>\n\n<p>The frames include 180 informative (<strong>I</strong>) video frames, 180 blurred (<strong>B</strong>)&nbsp;frames, 180 frames with saliva or specular reflections (<strong>S</strong>) and 180 underexposed (<strong>U</strong>) frames.</p>\n\n<p>The dataset was created for testing the method proposed in S. Moccia, et al. &quot;<em>Learning-based classification of informative laryngoscopic frames.</em>&quot; COMPUTER METHODS AND PROGRAM IN BIOMEDICINE, (accepted for publication).</p>\n\n<p>The folder<em>&nbsp;<strong>FRAMES.zip</strong>&nbsp;</em>contains 3 subfolders (FOLD1, FOLD2, FOLD3), which are the 3 folds used for cross-validation purpose in the frame&nbsp;classification performance assessment. Data separation in the folds is performed both at image- and patient-level.</p>\n\n<p>Each subfolder contains 4 folders relative to the four frame classes, i.e., <strong>I</strong>, <strong>B</strong>, <strong>S</strong> and <strong>U</strong>.</p>", 
  "license": "https://creativecommons.org/licenses/by-nc/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Istituto Italiano di Tecnologia / Politecnico di Milano", 
      "@id": "https://orcid.org/0000-0002-4494-8907", 
      "@type": "Person", 
      "name": "Sara Moccia"
    }, 
    {
      "affiliation": "Politecnico di Milano", 
      "@type": "Person", 
      "name": "Gabriele Omodeo Vanone"
    }, 
    {
      "affiliation": "Politecnico di Milano", 
      "@type": "Person", 
      "name": "Elena De Momi"
    }, 
    {
      "affiliation": "Istituto Italiano di Tecnologia", 
      "@type": "Person", 
      "name": "Leonardo S. Mattos"
    }
  ], 
  "url": "https://zenodo.org/record/1162784", 
  "datePublished": "2018-01-30", 
  "keywords": [
    "Frame selection, NBI endoscopy, machine learning, classification"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/e2ef067f-15d2-4876-bf04-6842bf6c7b8f/FRAMES.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.1162784", 
  "@id": "https://doi.org/10.5281/zenodo.1162784", 
  "@type": "Dataset", 
  "name": "NBI-InfFrames"
}