## 4 Discussion  
DCNNs can be seen as black-box models that output probabilities based on features learned during the training process, where it is difficult to control which features or patterns in the image are relevant. In contrast with previous work on VLE images [14; 15], where handcrafted features were selected based on visual properties with a clinical explanation, we have trained several DCNNs that provide a decision, based on learnable features from VLE images. Therefore, to observe the decisions of our trained DCNN, we computed the class activation maps (CAMs) [34], allowing us to observe which regions of the image were chosen as most important and discriminative. Examples of CAMs can be seen in Figure 5. We observe that for the HGD class, the activation maps mainly focus on concentration of glands that are located around the first layers of the esophagus. Similar conclusions were presented in the analysis reported by [PERSON] et al. [35]. Alternatively, we consider that the activation maps of the NDBE class indicate the homogeneity of the esophagus layers. These findings suggest a possible link between clinically-inspired features and the decisions learned by our DCNNs. More validation is needed to confirm these findings.  
Table 3 shows the differences between the reported studies. In comparison with [PERSON] et al. [14], and similar to [PERSON] et al. [15], we have obtained a dataset of _in-vivo_ VLE images, but with the following differences: (1) our dataset is different from [PERSON] et al. but closer to [PERSON] et al. and (2) our dataset is larger than the other two references ([PERSON] et al. compares only 18 patients). Our work improves in both the data acquisition and the evaluation method, by using a dataset of 22 in-vivo patients to uniquely train three DCNNs and further evaluate the results in a separate set of 23 in-vivo unseen patients. We use the AUC as the comparison metric because it is the most suitable value that better quantifies the work done in the aforementioned studies. Our work shows that we achieve the best reported results by utilizing a larger dataset in combination with state-of-the-art DCNNs. Moreover, in comparison with previous studies we take advantage of adjacent VLE frames to improve the classification results for a more robust prediction of neoplasia. Although our results are a positive step towards VLE interpretation, we have the opinion that an even larger dataset will provide more  
Figure 4: (a) Receiver operating characteristic curve for the three trained networks and the resulting ensemble validated on the external dataset. AUC denotes the area under the receiver operating characteristic curve. (b) Resulting confusion matrix for the external dataset.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Study Type** & **Analysis** & **Number of Patients** & **Evaluation Method** & **AUC** \\ \hline [PERSON] et al. [14] & Ex-vivo & Single-frame resection & 29, endoscopic resections & LOOCV & 0.91 \({}^{*}\) \\ [PERSON] et al. [15] & In-vivo & Single-frame VLE & 18, VLE laser-marked ROIs & LOOCV on training dataset & 0.86 \({}^{*}\) \\ Multi-frame LT and GS (Ours) & In-vivo & Multi-frame VLE & 18, VLE laser-marked ROIs & 4-fold CV on training dataset & 0.90 \\ Ensemble d DCNN (Ours) & In-vivo & Single-frame VLE & 45, VLE laser-marked ROIs & Validated with unseen test dataset & 0.91 \\ Ensemble of DCNN (Ours) & In-vivo & Multi-frame VLE & 45, VLE laser-marked ROIs & Validated with unseen test dataset & 0.96 \\ \hline \hline \end{tabular} \({}^{*}\) Best reported results. VLE volumetric laser endomicroscopy.  
\end{table}
Table 3: Evaluation of literature studies using volumetric laser endomicroscopy for detection of neoplasia in [PERSON]’s esophagus patients.  
insight into understanding VLE and will pave the way towards further improving early detection of EAC.