## 5 Conclusions  
[PERSON]'s esophagus (BE) is precursor of esophageal cancer, where detection of neoplasia in patients with this condition can enable early prevention and avoid further complications. We show that deep convolutional neural networks are capable of classifying a VLE region of interest between non-dysplastic BE (NDBE) and high-grade dysplasia (HGD). In this work we have trained several DCNNs to classify neoplasia in patients with BE and we evaluated them in a dataset of 45 patients. We obtained a specificity of 0.85, a sensitivity of 0.95 and an AUC of 0.96 on the test dataset, which clearly outperforms earlier work. Compared to earlier work, we took advantage of multi-frames of the in-vivo endoscopic acquisition to improve the confidence of our algorithm on predicting neoplasia. As far as our knowledge extends, our work is the first to train DCNNs on in-vivo VLE images and validate it using an unseen dataset. In our opinion, the presented study will improve the treatment of patients with BE by providing assistance to endoscopists. Our work could potentially aid clinicians towards a more accurate localization of regions of interest during biopsy extraction and provide an assessment to replace costly histopathological examinations.  
Conceptualization, R.F., T.S. and F.v.d.S.; methodology, R.F. and T.S.; software, R.F., T.S. and F.v.d.S.; validation, R.F. and T.S.; formal analysis, R.F and T.S; investigation, M.R.S. and A.J.d.G.; resources, W.L.C., E.J.S., J.J.G.H.M.B. and P.H.N.d.W.; data curation, M.R.S., A.J.d.G., W.L.C., E.J.S. and J.J.G.H.M.B.; writing-original draft preparation, R.F. and T.S.; writing-review and editing, F.v.d.S. and P.H.N.d.W.; visualization, R.F. and T.S.; supervision, F.v.d.S., J.J.G.H.M.B. and P.H.N.d.W.; project administration, F.v.d.S., J.J.G.H.M.B. and P.H.N.d.W.; funding acquisition, J.J.G.H.M.B. and P.H.N.d.W.  
This project has received funding from the European Union Research and Innovation Programme Horizon 2020 (H2020/2014-2020) under the Marie Sklodowska-Curie grant agreement No. 721766 (FBI Project). The collaboration project is financed by the Ministry of Economic Affairs of the Netherlands by means of the PPP Allowance made available by the Top Sector Life Sciences and Health to Amsterdam UMC to stimulate public-private partnerships.  
We gratefully acknowledge the collaboration of NinePoint Medical, Inc. and [PERSON] We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  
Figure 5: (Best viewed in color) Several VLE frames and its corresponding class activation maps (CAM). Images (**a**,**b**) belong to regions of interest (ROIs) with high-grade dysplasia (HGD), represented as class activation map (CAM) in images (**e**,**f**). Images (**c**,**d**) correspond to ROIs with non-dysplastic [PERSON]’s esophagagus (NDBE), with its CAM in images (**g**,**h**). Scale bars: 0.5 mm. Color bar (**a**–**d**): pixel intensity. Color bar (**e**–**h**): class activation intensity.  
Conflicts of Interest: Amsterdam UMC received funding from NinePoint Medical, Inc. for an investigator initiated study of VLE guided laser marking in [PERSON]'s esophagus. Amsterdam UMC and Eindhoven University of Technology received research funding from NinePoint Medical, Inc. for developing a computer assisted detection tool for VLE on which NinePoint Medical, Inc. holds the intellectual property.