## 4 Discussion  
Endoscopic diagnosis of early BE neoplasia is generally a two-step process of primary detection in overview, followed by detailed inspection of visible abnormalities using Narrow Band Imaging. However, endoscopists struggle with characterizing NBI-zoom imagery of subtle abnormalities, because NBI classifications are either too crude ('regular' vs. 'irregular') or consist of too many sub-classifications, which results in a subjective assessment. In adjunct to our earlier work on CAD analysis of WLE overview imagery for primary detection of neoplasia [9; 10; 16], this work improves on BE characterization using CAD analysis of NBI-zoom imagery. Here we have reported the first results of a well-developed and tested deep learning CAD system for the characterization of NBI-zoom videos of in BE. Most classification algorithms applied to the medical domain use models pretrained with ImageNet. This is a valid approach, considering that large data sets of more related imagery are usually not available. Additionally, models that employ pretraining typically outperform non-pretrained  
Figure 3: (**a**) ROC curves of the three ensemble models. (**b**) Example of dysplasia score over time. models. However, pretraining with ImageNet is not necessarily optimal. In previous work [15], we hypothesized that endoscopy-driven pretraining is preferable to more general pretraining with natural images. For the classification of NBI-zoom videos, in this work we provide empirical proof that endoscopy-driven pretraining outperforms not only the absence of pretraining, but standard pretraining as well. Moreover, ImageNet cannot be used for commercial use straightforwardly, which is essential when CAD algorithms are going to be deployed in clinical practice. High specificity is vital for successful clinical implementation of a CAD system for BE characterization in the second zoom-enhanced stage of the endoscopic diagnosis process. During surveillance endoscopies, endoscopists will most likely first be triggered by abnormal areas in overview, after which they will interrogate this area in detail for the presence of neoplasia using NBI. A characterization CAD system with high specificity would then be able to decrease the number of false-positive predictions by endoscopists, thereby reducing unnecessary biopsies. This system is vital for successful implementation of the primary detection system in WLE overview. Our previous work was conducted on high quality overview images in white light, captured by expert endoscopists. If this primary detection system is tested in real-life by non-expert endoscopists on endoscopic videos, the number of false-positive predictions would increase incrementally due to the presence of low quality and non-informative frames. In order to make this primary detection system more clinically-applicable, we propose a second NBI zoom algorithm to reduce the number of false positive predictions. Subsequently this may decrease the number of unnecessary biopsies, and thus decrease procedure time and increase cost-effectiveness. Additionally, the NBI zoom algorithm can investigate all potentially suspicious areas after the endoscopist or primary detection algorithm and thereby detect additional neoplastic areas. Work by [PERSON] et al. [6] has shown that good system performance can be achieved (85% accuracy) with an internally validated clinical classification system. Additionally, performance can be increased further when only assessing high confidence samples are evaluated (Accuracy 92%). However, an average high level of confidence among experts was achieved only in 62% of the examinations. This indicates that assessor inter-variability remains a problem for these clinical systems. While detection of dysplasia might be higher for experienced endoscopists for now, a CAD system as proposed in this work could aid practicing endoscopists in making better decisions. Although the performance of the proposed algorithm is promising, the current results do not yet support its application in clinical practice.