VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology

Abstract

Hematoxylin and eosin (H&E) staining is time-consuming, costly, hazardous, and prone to technician-dependent quality variations. This calls for fast, low-cost, and standardized computational alternatives. Lately, generative adversarial networks (GANs) have shown promising results by generating virtual stains from unstained tissue sections. However, no prior study has systematically benchmarked GANs for optimizing skin histology. Moreover, prior evaluations have focused mostly on the perceptual quality of virtual stains rather than their diagnostic utility. In this paper, we introduce VISGAB, a virtual staining-driven GAN benchmark. To our knowledge, it is the first to systematically evaluate and compare common GAN architectures for skin histology. We have also introduced a novel histology-specific fidelity index (HSFI), which focuses on diagnostic accuracy. VISGAB has been systematically applied to Cycle Consistent GAN (CycleGAN), Contrastive Unpaired Translation GAN (CUTGAN), and Dual Contrastive Learning GAN (DCLGAN) using the E-Staining DermaRepo skin histology dataset. The dataset contains 87 whole-slide images (WSIs) of normal, carcinoma, and inflammatory dermatoses tissues. VISGAB findings identify CycleGAN with superior structural fidelity (SSIM: 0.93, HSFI: 0.81), diagnostic sufficiency (75% nuclear atypia detection), and Turing test success (81%), despite higher mean inference time (~ 1.96 min) and mode collapse risk (~ 25%). Although CUTGAN and DCLGAN offer faster training, artifacts (blurring, overstaining, hallucinations) limit their diagnostic utility. Qualitative evaluations by experts and statistical rigor further substantiate our findings in favor of CycleGAN. This work supports AI-driven histopathology by addressing critical gaps in the literature.