Histopathology refers to the observation of tissues to identify the manifestation of diseases, e.g., cancer. Tiny tissue samples are taken from the patient and studied through a microscope; the analysis of the different cells, particularly their nuclei and other structures, allows for disease detection. The biological specimens need some preparation, namely Hematoxylin and Eosin (H&E) staining is often used to highlight nuclei and cytoplasm. Although staining is fundamental, given that cells are transparent when imaged, it is still highly affected by casual errors: colors change when a small preparation step is slightly different and even when a different microscope is used. This factor leads to Computer Aided Detection (CAD) systems losing performance. Therefore, to solve this problem and allow for the integration of multiple low-dimensional datasets, we propose a CycleGAN-based architecture exploiting PatchGAN and U-Net backbones as discriminators and generators, respectively, demonstrating an improvement in mean Structural Similarity Index Measure (SSIM) over the one computed on the original datasets of around 1.8%.