Unpaired H&E to Pr Stain Transfer with Self-Supervised Auxiliary Segmentation

Histochemical staining is a critical step in the diagnosis of cancer, where hematoxylin-eosin (H&E) stain is used most commonly in clinical practice. However, the H&E images often cannot be used for making accurate diagnoses. To this end, pathologists must perform immunohistochemical (IHC) stain, which is time-consuming and costly. In the field of computer-aided diagnosis, existing models can virtually generate IHC staining images, but they often require pixel-aligned data and annotations from pathologists, which are difficult to be obtained. To address this problem, we propose a self-supervised PR (a typical type of IHC) virtual staining model utilizing unpaired data without pathologists’ annotations for the first time. Based on the observation that PR images are easy to be segmented, we introduce segmentation as the proxy task to make the virtual staining more accurate. Experimental results show that our model can generate PR images with the highest accuracy. Moreover, our model achieves the desired results on an external dataset.