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<p>Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the state-of-the-art in virtual staining for breast cancer biomarkers (HER2, PgR, ER and Ki-67) and benchmarks several models on public datasets. It serves as a resource for researchers and clinicians interested in applying or developing virtual staining techniques.</p>

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H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Pascal Klöckner,
  • José Teixeira,
  • Diana Montezuma,
  • João Fraga,
  • Hugo M. Horlings,
  • Jaime S. Cardoso,
  • Sara P. Oliveira

摘要

Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the state-of-the-art in virtual staining for breast cancer biomarkers (HER2, PgR, ER and Ki-67) and benchmarks several models on public datasets. It serves as a resource for researchers and clinicians interested in applying or developing virtual staining techniques.