DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
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Abstract (arXiv)
Breast cancer is a highly fatal disease among cancers in women, and early detection is crucial for treatment. HER2 status, a valuable diagnostic marker based on Immunohistochemistry (IHC) staining, is instrumental in determining breast cancer status. The high cost of IHC staining and the ubiquity of Hematoxylin and Eosin (H&E) staining make the conversion from H&E to IHC staining essential. In this article, we propose a destain-restain framework for converting H&E staining to IHC staining, leveraging the characteristic that H&E staining and IHC staining of the same tissue sections share the Hematoxylin channel. We further design loss functions specifically for Hematoxylin and Diaminobenzidin (DAB) channels to generate IHC images exploiting insights from separated staining channels. Beyond the benchmark metrics on BCI contest, we have developed semantic information metrics for the HER2 level. The experimental results demonstrated that our method outperforms previous open-sourced methods in terms of image intrinsic property and semantic information.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 1 Sep 2024.
Builds on
6 of 17 references.
References · 6
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
- 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
- 2022BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixcited by 61
- 2023Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairscited by 38
- 2023Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Reviewcited by 7
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Citing papers per year: 2024: 1.
1 paper.