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

Muhammad Altaf Hussain1, Muhammad Asim Waris2, Muhammad Usman Akram3

  • 1Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan. altaf42049@gmail.com.

Scientific Reports
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces VISGAB, a benchmark for virtual staining using generative adversarial networks (GANs) in skin histology. CycleGAN demonstrated superior diagnostic utility and structural fidelity compared to other GANs, supporting AI-driven histopathology.

Keywords:
CUTGANCycleGANDCLGANHSFIImage to image translationVirtual staining

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Area of Science:

  • Computational pathology
  • Digital histopathology
  • Artificial intelligence in medicine

Background:

  • Hematoxylin and eosin (H&E) staining is a standard but flawed method in skin histology.
  • Current limitations include time, cost, hazards, and quality variations.
  • Generative adversarial networks (GANs) offer potential for virtual staining but require systematic evaluation for diagnostic utility.

Purpose of the Study:

  • To introduce VISGAB, the first benchmark for evaluating GANs in skin histology.
  • To systematically compare common GAN architectures for virtual staining.
  • To assess virtual stains based on diagnostic utility, not just perceptual quality.

Main Methods:

  • Development and application of the VISGAB benchmark.
  • Systematic evaluation of CycleGAN, CUTGAN, and DCLGAN on the DermaRepo skin histology dataset (87 WSIs).
  • Utilized histology-specific fidelity index (HSFI) and expert qualitative evaluations.

Main Results:

  • CycleGAN achieved superior structural fidelity (SSIM: 0.93, HSFI: 0.81) and diagnostic sufficiency (75% nuclear atypia detection).
  • CycleGAN also showed high Turing test success (81%) despite longer inference times and mode collapse risk.
  • CUTGAN and DCLGAN exhibited artifacts limiting their diagnostic utility.

Conclusions:

  • CycleGAN is the preferred GAN architecture for virtual skin histology, balancing fidelity and diagnostic accuracy.
  • VISGAB provides a robust framework for evaluating AI-driven histopathology tools.
  • This research addresses critical gaps in benchmarking GANs for reliable diagnostic applications.