IBSU-1432 dataset on videoendoscopy frame quality classification for laryngoscopy (NBI modality)

<strong>How to cite</strong> Nogal, P., Buchwald, M., Staśkiewicz, M., Kupiński, S., Pukacki, J., Mazurek, C., ... &amp; 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. https://doi.org/10.5604/01.3001.0015.9501 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. https://doi.org/10.3389/fonc.2021.626602 Moccia, S., Vanone, G. O., Momi, E. De, Laborai, A., Guastini, L., Peretti, G., &amp; Mattos, L. S. (2018). Learning-based classification of informative laryngoscopic frames. <em>Computer Methods and Programs in Biomedicine</em>, <em>158</em>, 21–30. https://doi.org/10.1016/j.cmpb.2018.01.030 <strong>Description</strong> The presented dataset consists of 1432 laryngeal endoscopy frames of different acquisition quality. The four classes were distinguished (after Moccia et al., 2018): Informative frames (436), Blurred (383), Saliva/specular reflections (321), and Underexposed frames (292). (In total, 1432 = 436 I + 383 B + 321 S + 292 U.) <strong>Acknowledgements</strong> Alberto Paderno, MD PhD – Brescia data part Sara Moccia, PhD – IBSU-720 frames dataset from Zenodo: https://zenodo.org/record/1162784#.Ycrmfi1Q1qt Małgorzata Wierzbicka, MD PhD – Poznan data part Piotr Nogal, MD – Poznan data part Joanna Jackowska, MD PhD – Poznan data part Hanna Klimza, MD PhD – Poznan data part