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4 results for “videoendoscopy”
IBSU-1432 dataset on videoendoscopy frame quality classification for laryngoscopy (NBI modality)
<p><strong>How to cite</strong></p> <p>Nogal, P., Buchwald, M., Staśkiewicz, M., Kupiński, S., Pukacki, J., Mazurek, C., ... & Wierzbicka, M. (2022). Endoluminal larynx anatomy model–towards facilitating deep learning and defining standards for medical images evaluation with artificial intelligence algorithms. <em>Polish Journal of Otolaryngology</em>, <em>76</em>(5), 37-45. <a href="https://doi.org/10.5604/01.3001.0015.9501">https://doi.org/10.5604/01.3001.0015.9501</a></p> <p>Paderno, A., Piazza, C., Del Bon, F., Lancini, D., Tanagli, S., Deganello, A., … Moccia, S. (2021). Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective. <em>Frontiers in Oncology</em>, <em>11</em>(March), 1–12. <a href="https://doi.org/10.3389/fonc.2021.626602">https://doi.org/10.3389/fonc.2021.626602</a></p> <p>Moccia, S., Vanone, G. O., Momi, E. De, Laborai, A., Guastini, L., Peretti, G., & Mattos, L. S. (2018). Learning-based classification of informative laryngoscopic frames. <em>Computer Methods and Programs in Biomedicine</em>, <em>158</em>, 21–30. <a href="https://doi.org/10.1016/j.cmpb.2018.01.030">https://doi.org/10.1016/j.cmpb.2018.01.030</a></p> <p><strong>Description</strong></p> <p>The presented dataset consists of 1432 laryngeal endoscopy frames of different acquisition quality. The four classes were distinguished (after Moccia et al., 2018):</p> <ol> <li>Informative frames (436),</li> <li>Blurred (383),</li> <li>Saliva/specular reflections (321), and</li> <li>Underexposed frames (292).</li> </ol> <p>(In total, 1432 = 436 I + 383 B + 321 S + 292 U.)</p> <p><strong>Acknowledgements</strong></p> <p>Alberto Paderno, MD PhD – Brescia data part</p> <p>Sara Moccia, PhD – IBSU-720 frames dataset from Zenodo: <a href="https://zenodo.org/record/1162784#.Ycrmfi1Q1qt">https://zenodo.org/record/1162784#.Ycrmfi1Q1qt</a></p> <p>Małgorzata Wierzbicka, MD PhD – Poznan data part</p> <p>Piotr Nogal, MD – Poznan data part</p> <p>Joanna Jackowska, MD PhD – Poznan data part</p> <p>Hanna Klimza, MD PhD – Poznan data part</p>
A Randomized Study Comparing Autofluorescence Imaging(AFI) Followed by Narrow Band Imaging(NBI) With Videoendoscopy for the Detection of High Risk Lesions of Stomach
ClinicalTrials.gov study NCT01132534. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Predicting segmentation quality in laryngeal videoendoscopy
Open the record for dataset details and reuse information.
Comparison Between HD+ Endoscopy and Standard Videoendoscopy in Screening Colonoscopy
ClinicalTrials.gov study NCT00792649. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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