Diffusion-based virtual multi-stain staining for pituitary adenoma histopathology

Abstract

Background and Objective:

Immunohistochemistry (IHC) for lineage-defining transcription factors—such as Pit1, Tpit, and SF1—together with markers like Ki67 and ACTH, is central to classifying pituitary neuroendocrine tumors and guiding management. Yet preparing multiple IHC slides beyond hematoxylin–eosin (H&E) is costly and inconsistent across sites. We aim to produce clinically reliable virtual IHC stains from a single H&E image under unpaired supervision.

Methods:

We develop a diffusion-based one-to-many virtual staining framework that transforms a single H&E image into five key IHC stains. Our approach fuses multi-scale structural features into a unified conditional representation, processed by a shared backbone with stain-specific decoders to enable mutually reinforcing learning across stains. The model incorporates perception-prioritized, timestep-aware diffusion loss and a stain-consistency term during training, combined with classifier-guided sampling during inference, to achieve exceptional fidelity in both cellular structure and chromogenic style. For clinical validation, we further introduced the nuclear positivity rate as a slide-level endpoint to quantitatively evaluate agreement between virtual and real IHC.

Results and Conclusion:

Compared to the best-performing baseline, our method achieves superior pixel and perceptual fidelity (PSNR $\uparrow$ +0.94 dB, SSIM $\uparrow$ +0.015; FID $\downarrow$ $-7.8\text{\%}$, LPIPS $\downarrow$ $-7.9\text{\%}$). For the clinical endpoint, averaged across all stains, it delivers substantial improvements in mean absolute error, correlation, and accuracy relative to the baseline average—reducing MAE by approximately 28.7%, increasing correlation by 0.07, and boosting accuracy by 0.05. Coupled with roughly 80% fewer parameters and comparable throughput, these outcomes position our approach as a unified, clinically focused diffusion model for virtual multi-stain synthesis.