Stain transfer using CycleGAN for histopathological images

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%.