## 4 Conclusions  
In this work, we proposed a new approach for training GANs using XAI to improve generation quality and data augmentation performance on histopathological datasets. We used XAI methods, such as Saliency, DeepLIFT, and Gradient\(\odot\)Input, to extract feature information from the discriminator and feed it to the generator during the training. We evaluated the proposed method on four histopathological datasets, CR, LA, LG, and UCSB, using the FID and IS metrics to assess the quality of generated images and the accuracy metric to compare the classification performance of four Transformer models, ViT, DEiT, PVT, and CoAtNet, with and without data augmentation. The multiple experiments provided a solid foundation to understand the effectiveness of our approach in this specific domain. The results showed that the proposed method increased the quality and diversity of the generated images. In most cases, the XGAN provided better FID and IS values than traditional GAN models. For instance, it was possible to decrease the FID by up to 32.70% compared to the traditional architectures. This gain in quality positively affected the classification performance of the Transformer models. Accuracy was increased by up to \begin{table}
\begin{tabular}{l c c} \hline \hline
**Position** & **Combination** & **Number of Best Cases** \\ \hline
1 & XWGAN-GP + Saliency & 7 \\
2 & XDCGAN + Saliency & 6 \\
3 & XRAGAN + Saliency & 4 \\
4 & XWGAN-GP + Gradient\(\odot\)Input & 5 \\
5 & XDCGAN + Gradient\(\odot\)Input & 4 \\
6 & XRAGAN + Gradient\(\odot\)Input & 3 \\
6 & XRAGAN + DeepLIFT & 3 \\
7 & XWGAN + DeepLIFT & 1 \\
7 & XDCGAN + DeepLIFT & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ranking of the combinations, showing best cases. 3.81% compared to the models without data augmentation and up to 3.01% compared to the models with traditional GAN data augmentation. We also showed that Saliency was the best method for providing information to the generator, followed by Gradient\(\odot\)Input and DeepLIFT. The XWGAN-GP + Saliency combination was a highlight, as it outperformed WGAN-GP in seven cases. These results are significant because they show XAI's potential to improve the quality of image generation by using GANs on histopathological datasets. We demonstrated that the features provided by XAI explanations contribute to better generalization in the training of Transformer models, promoting an improvement in their classification power. Also, we identified that the saliency method provided the best features and was the most relevant method for composing a combination with GAN models. These findings provide insights and guidelines for researchers and experts interested in developing artificial augmentation techniques for histopathological datasets. In future work, some insights can be investigated: 1. new tests using different combinations of GAN models; 2. apply other evaluation metrics, such as the precision-recall metrics, for generative models; 3. integrate MLOps approaches and pipelines for biomedical image processing; 4. tests using larger datasets with a significantly higher number of samples, including those from different medical domains, to comprehensively evaluate the performance of our proposed method and its generalization capabilities; 5. new analysis by delving deeper into the reasons behind the varying performance of different GAN and XAI combinations to provide a more thorough examination of their specific advantages and limitations; 6. statistical analysis, including significance testing, to verify the improvements in classification performance and complexity analysis of the proposed method to guide optimization processes. Conceptualization, methodology, G.B.R., A.L., M.Z.d.N. and L.A.N. ; software, G.B.R., B.L.d.O.G. and V.A.T.B. ; validation, formal analysis, investigation, G.B.R., A.L., B.L.d.O.G., V.A.T.B. and L.A.N. ; writing--original draft preparation, G.B.R. ; writing--review and editing, G.B.R., A.L., T.A.A.T., M.Z.d.N. and L.A.N. ; funding acquisition, G.B.R., M.Z.d.N. and L.A.N.