Dataset Open Access
Sara Moccia;
Gabriele Omodeo Vanone;
Elena De Momi;
Leonardo S. Mattos
{ "publisher": "Zenodo", "DOI": "10.5281/zenodo.1162784", "author": [ { "family": "Sara Moccia" }, { "family": "Gabriele Omodeo Vanone" }, { "family": "Elena De Momi" }, { "family": "Leonardo S. Mattos" } ], "issued": { "date-parts": [ [ 2018, 1, 30 ] ] }, "abstract": "<p>The <strong>NBI-InfFrames </strong>dataset<strong> </strong>aims to provide the surgical data science community with a labeled dataset for the identification of informative endoscopic video frames. </p>\n\n<p>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). </p>\n\n<p>The frames include 180 informative (<strong>I</strong>) video frames, 180 blurred (<strong>B</strong>) 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. "<em>Learning-based classification of informative laryngoscopic frames.</em>" COMPUTER METHODS AND PROGRAM IN BIOMEDICINE, (accepted for publication).</p>\n\n<p>The folder<em> <strong>FRAMES.zip</strong> </em>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.</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>", "title": "NBI-InfFrames", "type": "dataset", "id": "1162784" }
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