## 4 Discussion  
For experts with no experience in training deep networks, observing the evaluation of training and test data can provide useful knowledge. Table 2 summarizes the pre-trained model performances on the training set. It is common for train data to have higher values  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Specificity** & **Recall** & **F1** \\ \hline VGG16 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ VGG19 & 0.9998 & 0.9994 & 0.9997 & 1.000 & 0.9997 \\ NASNetLarge & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ Xception & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ InceptionResNetV2 & 0.9998 & 0.9994 & 0.9997 & 1.000 & 0.9997 \\ DenseNet201 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pre-trained Models’ Results on the Train Set.  
Figure 5: UMAP embedding space of Xception on training and test set. (**a**) shows _cross-section_ and _other_ labeled data from the training set, (**b**) shows _cross-section_ and _other_ labeled data from test set, (**c**) illustrates both labeled train and test data points in the same representation space.  
than test data. As Table 1 shows DenseNet201 has the best results for the test set, even though it is not the best performing model on the ImageNet among the ones that we have tested.  
In addition to the numerical assessment, visualizations also give an indication of a properly constructed pipeline. Figures 4 and 5 show that the data split was appropriate and highlights minor differences between the pre-trained models.  
As it is shown in Tables 1 and 2, the difference between the worst performing model VGG19, and the best performing model DenseNet201 is less than 0.8%. This phenomenon does not imply that DenseNet201 will always perform better than the other models for every case. We conduct that DenseNet201 has a good chance of performing well on similar datasets. Consequently, this also suggests that VGG19 will not perform well on a similar dataset. This experience can be gained by the domain expert themselves without any prior knowledge of the underlying architecture of the models.  
Simple methods can support the work of domain experts to further improve data management tasks beyond labelling. For example, one can search for images or groups of images similar to a given image by measuring the distances between latent representations of images that can be incorporated into our pipeline. Another approach would be to search for anomalies by finding the least similar images in the dataset, or images representing a rare subcategory.  
Generative Adversarial Networks (GANs) [36] and Variational Autoencoders (VAEs) [37] are becoming increasingly popular in anomaly detection tasks. The use of these algorithms is a promising opportunity for further improvements to expand our pipeline. Yet, the integration of such methods need care as they may involve training and need artificial intelligence specialists.  
Although the case we studied brought about high-quality results, there are some possible limitations to it. For instance, it heavily relies on pre-trained models for feature extraction, meaning those features are extracted from the ImageNet dataset tuned for the classes of the dataset. In turn, the selection of the final layer could not be the optimal strategy for all datasets. However, in the case of this particular cooperation, this technique achieved the excellent results. It is hard to predict how the method will perform on other datasets. Another aspect that could be considered a downside is the need for human intervention in various stages of the pipeline. The current methodology design allows the experts to directly examine and interact with the data to decide the best course of action based on their experience. However, if the extreme high level of accuracy is not required, such careful examination could be deemed unnecessary.