October 6, 2017 Dataset Open Access

Sara Moccia; Elena De Momi; Leonardo S. Mattos

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    "description": "<p>The dataset in <em><strong>laryngeal dataset.tar </strong></em>contains 1320 patches of healthy and early-stage cancerous laryngeal tissues. The patches (100x100 pixels) were manually extracted from 33 narrow-band laryngoscopic images of 33 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination).</p>\n\n<p>Specifically, four tissue classes were considered (330 patches/tissue class): He (healthy tissue), Hbv (tissue with hypertrophic vessels), Le (tissue with leukoplakia) and IPCL (tissue with intrapapillary capillary loops).</p>\n\n<p>The dataset was created for testing the method proposed in <em>Moccia, Sara, et al. \"Confident texture-based laryngeal tissue classification for early stage diagnosis support.\" JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.</em></p>\n\n<p>The folder<em> <strong>laryngeal dataset.tar</strong> </em>contains 3 subfolders (FOLD 1, FOLD 2, FOLD 3), which are the 3 folds used for cross-validation purpose in the tissue classification performance assessment.</p>\n\n<p>Each subfolder contains 4 folders relative to the four tissue classes, i.e., Le, He, Hbv, IPCL.</p>\n\n<p>----------------------------------------------------------------------------------------------------------------------------------------------------------</p>\n\n<p>If you want to use the dataset, please cite <em>Moccia, Sara, et al. \"Confident texture-based laryngeal tissue classification for early stage diagnosis support.\" JOURNAL OF MEDICAL IMAGING 4.03 (2017): 1-10.</em></p>\n\n<p>\u00a0</p>\n\n<p>\u00a0</p>\n\n<p>\u00a0</p>", 
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        "affiliation": "Politecncico di Milano, Istituto Italiano di Tecnologia", 
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        "affiliation": "Politecncico di Milano", 
        "name": "Elena De Momi"
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        "affiliation": "Istituto Italiano di Tecnologia", 
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