用生成模型实现跨机构病理图像无注册染色转换，无需重训练。
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
- 通过直方图归一化与通道校准预处理，提升模型跨机构泛化能力。
- 虚拟去染色的皮尔逊相关系数达0.854，重染色性能优于直接染色。
- 适合病理图像跨中心应用，需关注恶性腺体形态失真问题。
条件生成对抗网络（cGAN）已实现数字病理全片扫描图像（WSI）中苏木精-伊红（H&E）染色的高保真计算染色与去染色。然而，其在无注册、跨机构的外部数据上泛化能力仍不明确。此前在布里格姆妇女医院102例配准前列腺核心活检WSI上训练的cGAN模型，在斯坦福大学采集的82例空间未配准的WSI上进行评估。为避免再训练，开发了基于直方图的H&E染色图像归一化与未染色图像通道强度校准的预处理流程。由于有意省略图像配准以模拟真实部署场景，报告结果为保守下限，同时反映模型性能与空间对齐限制。虚拟去染色达到皮尔逊相关系数（PCC）0.854，结构相似性指数（SSIM）0.699，峰值信噪比（PSNR）18.41 dB。从计算去染色输出重建H&E染色的表现优于直接从真实未染色输入染色（PCC: 0.798 vs. 0.715；SSIM: 0.756 vs. 0.718；PSNR: 20.08 vs. 18.51 dB），表明预处理质量可能比模型容量更关键。定性病理评审显示良性腺体结构得以保留，但恶性腺体常被错误渲染为血管样形态。这些发现支持仅通过预处理即可将cGAN基计算染色/去染色模型应用于外部WSI数据集，同时明确了未来领域自适应需关注的具体形态目标。
原文摘要 · Abstract (English)
Conditional generative adversarial networks (cGANs) have enabled high-fidelity computational staining and destaining of hematoxylin and eosin (H&E) in digital pathology whole-slide images (WSI). However, their ability to generalize to out-of-distribution WSI across institutions without retraining remains insufficiently characterized. Previously developed cGAN models trained on 102 registered prostate core biopsy WSIs from Brigham and Women's Hospital were evaluated on 82 spatially unregistered WSIs acquired at Stanford University. To mitigate domain shift without retraining, a preprocessing pipeline consisting of histogram-based stain normalization for H&E-stained WSIs and channel-wise intensity calibration for unstained WSIs was developed. Because image registration was intentionally omitted for real-world deployment conditions, the reported quantitative results are conservative lower bounds reflecting both model performance and limited spatial alignment. Under these conditions, virtual destaining achieved a Pearson correlation coefficient (PCC) of 0.854, structural similarity index measure (SSIM) of 0.699, and peak signal-to-noise ratio (PSNR) of 18.41 dB. H&E restaining from computationally destained outputs outperformed direct staining from ground-truth unstained inputs across all metrics (PCC: 0.798 vs. 0.715; SSIM: 0.756 vs. 0.718; PSNR: 20.08 vs. 18.51 dB), suggesting that preprocessing quality may be more limiting than model capacity. Qualitative pathological review indicated preservation of benign glandular structures while showing that malignant glands were often rendered with vessel-like morphologies. These findings support the feasibility of applying cGAN-based computational H&E staining and destaining generative models to external WSI datasets using preprocessing-based adaptation alone while defining specific morphological targets for future domain adaptation.
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