Leveraging Adversarial Learning for Pathological Fidelity in Virtual Staining

TL;DR

This paper introduces CSSP2P GAN, a novel virtual staining model that leverages adversarial learning to improve pathological fidelity, validated through expert evaluation and highlighting the limitations of traditional metrics.

Contribution

We develop CSSP2P GAN, demonstrating the importance of adversarial loss for high-quality virtual staining and critically assess evaluation metrics in the field.

Findings

CSSP2P GAN achieves higher pathological fidelity according to expert evaluation.

Adversarial loss significantly improves virtual staining quality.

Traditional metrics like SSIM and PSNR are insufficient for evaluating virtual stains.

Abstract

In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just in 2024. Most studies use publicly available datasets of H&E-IHC pairs from consecutive tissue sections. Recognizing the training challenges, many authors develop complex virtual staining models based on conditional Generative Adversarial Networks, but ignore the impact of adversarial loss on the quality of virtual staining. Furthermore, overlooking the issues of model evaluation, they claim improved performance based on metrics such as SSIM and PSNR, which are not sufficiently robust to evaluate the quality of virtually stained images. In this paper, we developed CSSP2P GAN, which we demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation. Furthermore, while iteratively developing our model, we study the impact of the adversarial loss and demonstrate its crucial role in the quality of virtually stained images. Finally, while comparing our model with reference works in the field, we underscore the limitations of the currently used evaluation metrics and demonstrate the superior performance of CSSP2P GAN.