The endoscopic examination of subepithelial vascular variations of vocal folds can provide complementary diagnostic information for clinicians regarding the development of benign and malignant laryngeal lesions. As one novel technique, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) can provide real-time and enhanced visualization of these vascular structures. Several studies have addressed the concern of subjective evaluation of CE-NBI images, resulting in the development of multiple computer-based solutions. We introduce the CE-NBI data set, the first publicly available data set with enhanced and magnified visualization of vocal fold subepithelial blood vessels. It comprises 11144 images of 210 adult patients with benign and malignant lesions in the vocal fold. Image annotations include as following for all images of every patient: Diagnosed laryngeal histopathology label. Lesion type benign-malignant label. Leukoplakia diagnosis label. The dataset consists of two main categories: benign and malignant images. In each category, the images of every patient are ordered according to the laryngeal histopathology class. Additionally, one Excel file is provided to map the image files of each patient to three image labels and image dimensions. This data has successfully been used to perform clinical evaluations as well as design and develop multiple Machine Learning (ML)-based algorithms for laryngeal cancer assessment.

{"references": ["Esmaeili, Nazila, et al. \"Novel automated vessel pattern characterization of larynx contact endoscopic video images.\" International journal of computer assisted radiology and surgery 14.10 (2019): 1751-1761.", "Davaris, Nikolaos, et al. \"Evaluation of vascular patterns using contact endoscopy and narrow-band imaging (CE-NBI) for the diagnosis of vocal fold malignancy.\" Cancers 12.1 (2020): 248.", "Esmaeili, Nazila, et al. \"Laryngeal lesion classification based on vascular patterns in contact endoscopy and narrow band imaging: manual versus automatic approach.\" Sensors 20.14 (2020): 4018.", "Esmaeili, Nazila, et al. \"Cyclist effort features: A novel technique for image texture characterization applied to larynx cancer classification in contact endoscopy\u2014Narrow band imaging.\" Diagnostics 11.3 (2021): 432.", "Esmaeili, Nazila, et al. \"Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging.\" Sensors 21.23 (2021): 8157."]}