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<p>Accurate histopathological diagnosis typically relies on multiple chemical stains, a process that is labor-intensive, tissue-consuming, and environmentally taxing. While virtual staining offers a faster, tissue-conserving alternative, its clinical adoption is hindered by the requirement for perfectly aligned paired data, which is difficult to obtain due to tissue distortion during chemical processing. We present a robust virtual staining framework that mitigates spatial mismatches through a cascaded registration mechanism. By decoupling image generation from spatial alignment, our method enables high-fidelity staining even from imperfectly paired or misaligned datasets without altering existing model architectures. Our approach significantly outperforms state-of-the-art models across five datasets, showing a remarkable 23.8% improvement in image quality for highly misaligned samples. In blinded evaluations, experienced pathologists achieved 52% accuracy in distinguishing virtual from chemical stains, indicating that the two were indistinguishable. This framework simplifies data acquisition and provides a scalable pathway for integrating virtual staining into routine clinical workflows.</p>

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Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Jiabo Ma,
  • Wenqiang Li,
  • Jinbang Li,
  • Ziyi Liu,
  • Linshan Wu,
  • Fengtao Zhou,
  • Li Liang,
  • Ronald Cheong Kin Chan,
  • Terence T. W. Wong,
  • Hao Chen

摘要

Accurate histopathological diagnosis typically relies on multiple chemical stains, a process that is labor-intensive, tissue-consuming, and environmentally taxing. While virtual staining offers a faster, tissue-conserving alternative, its clinical adoption is hindered by the requirement for perfectly aligned paired data, which is difficult to obtain due to tissue distortion during chemical processing. We present a robust virtual staining framework that mitigates spatial mismatches through a cascaded registration mechanism. By decoupling image generation from spatial alignment, our method enables high-fidelity staining even from imperfectly paired or misaligned datasets without altering existing model architectures. Our approach significantly outperforms state-of-the-art models across five datasets, showing a remarkable 23.8% improvement in image quality for highly misaligned samples. In blinded evaluations, experienced pathologists achieved 52% accuracy in distinguishing virtual from chemical stains, indicating that the two were indistinguishable. This framework simplifies data acquisition and provides a scalable pathway for integrating virtual staining into routine clinical workflows.