## 1 Introduction  
Barrett's Esophagus (BE) is a known precursor for Esophageal AdenoCarcinoma (EAC), a form of gastrointestinal cancer with a particularly poor prognosis. For this reason, patients with BE undergo regular endoscopic surveillance. This offers early detection of neoplasia and enables endoscopic treatment, which has an excellent prognosis [1; 2]. However, the current BE surveillance protocol is suboptimal, mainly due to the fact that neoplasia is difficult to detect because of their subtle endoscopic appearances [1]. Additionally, BE surveillance is time consuming, expensive, and has a high potential for sampling error of random biopsies [3].  
Generally, endoscopic diagnosis is established in two stages. First, primary detection of a suspected lesion is determined with White Light Endoscopy (WLE) in overview, followed by a detailed inspection of the mucosa for any visible abnormalities and to identify the lesion boundaries. This inspection is often performed using Narrow Band Imaging (NBI), an imaging modality that exploits light of specific blue and green wavelengths to enhance the visible details of e.g., the surface texture of the mucosa. More specifically, NBI is used in combination with a magnified view to improve the visualization of mucosal and vascular patterns. Several different NBI clinical classification systems have been proposed, which involve complicated and diverse criteria, all of which have demonstrated suboptimal diagnostic accuracy and inter-observer agreement [4; 5; 6]. In daily practice, endoscopists performing BE surveillance struggle with the evaluation of NBI imagery in magnified view for characterization of BE neoplasia. New advances in Computer-Aided Detection (CAD), more specifically deep learning techniques, may be able to overcome limitations related to the varying observations of different endoscopists, and thereby improve on the diagnostic accuracy for detailed inspection.  
Especially deep learning techniques have shown promising results in a variety of different scientific research domains, including gastrointestinal endoscopy [7; 8; 9]. Recently, our group demonstrated a highly effective computer-aided detection system, that recognizes and localizes BE neoplasia on WLE overview images in real time with high accuracy, enabling primary detection [10]. The aim of the current study is to investigate the feasibility of a deep learning algorithm for the characterization of NBI-zoom imagery after primary detection in WLE.  
To our knowledge, there is no prior art pertaining to the classification of early cancerous lesions in BE using NBI-zoom videos. However, other work has investigated the use of NBI-zoom _still images_[11] or NBI in overview [12] for the classification of colorectal tumors. In earlier work, we initially explored extracting the additional information contained in multi-modal imaging, using deep learning and a combination of NBI and WLE in overview, to improve localization scores for dysplasia in BE [13]. [PERSON] et al. [14] performed a similar classification study using both WLE as well as NBI images, using the near-focus functionality of the endoscope. However, results in these studies were preliminary and based on small image-based data sets to illustrate a proof of concept.  
In this work, a large data set of NBI-zoom imagery is used for the classification of dysplasia in BE. In contrast to prior work, endoscopic zoom _videos_, rather than still images, are used to improve the classification performance. Additionally, this work does not rely on ImageNet pretraining. Instead, it builds further upon endoscopy-driven pretraining, as introduced in previous work [15; 16]. Networks that do not depend on ImageNet pretraining are preferable, since there are licences associated with the ImageNet data set. In this work, we give another empirical example in favor of endoscopic pretraining. Besides this, we show the benefit of a pseudo-labeled bootstrap ensemble for classification of NBI-zoom videos.