Impact of Loss Functions on Label-free Virtual H&E Staining

Label-free virtual Haematoxylin and Eosin (H&E) staining has the potential to generate realistic histological images for rapid clinical diagnosis, eliminating the need for the conventional, costly, and time-consuming tissue staining procedure. Although various deep learning techniques have proven effective for this purpose, there has been limited attention given to how different loss functions influence the fidelity of synthesis. In this study, we focus on assessing the qualitative and quantitative impact of four widely applied loss functions designed for high-fidelity image synthesis in the context of virtual H&E staining using single-channel label-free autofluorescence images. Qualitative analysis involves a visual comparison between true and synthetic images, while quantitative analysis utilises several well-known image similarity metrics to measure the distance between real and virtual images. Our experimental results demonstrate the feasibility of extra regularisation terms with different weights on synthesis H&E images. Both visual inspection and quantitative outcomes align well with each other, but both should be facilitated to reach a conclusive decision for the virtual staining with optimal quality.