Cross-channel Perception Learning for H&E-to-IHC Virtual Staining

• URL: http://arxiv.org/abs/2506.07559v1
• Date: Mon, 09 Jun 2025 08:54:15 GMT
• Title: Cross-channel Perception Learning for H&E-to-IHC Virtual Staining
• Authors: Hao Yang, JianYu Wu, Run Fang, Xuelian Zhao, Yuan Ji, Zhiyu Chen, Guibin He, Junceng Guo, Yang Liu, Xinhua Zeng,
• Abstract summary: Cross-Channel Perception Learning (CCPL) strategy decomposes HER2chemical staining into Hematoxylin and DAB staining channels.<n> CCPL extracts dual-channel features from both the generated and real images and measures cross-channel correlations between nuclei and membranes.<n>Results: CCPL effectively preserves pathological features, generates high-quality virtual stained images, and provides robust support for automated pathological diagnosis using multimedia medical data.
• Score: 16.604685889132995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of digital pathology, virtual staining has become a key technology in multimedia medical information systems, offering new possibilities for the analysis and diagnosis of pathological images. However, existing H&E-to-IHC studies often overlook the cross-channel correlations between cell nuclei and cell membranes. To address this issue, we propose a novel Cross-Channel Perception Learning (CCPL) strategy. Specifically, CCPL first decomposes HER2 immunohistochemical staining into Hematoxylin and DAB staining channels, corresponding to cell nuclei and cell membranes, respectively. Using the pathology foundation model Gigapath's Tile Encoder, CCPL extracts dual-channel features from both the generated and real images and measures cross-channel correlations between nuclei and membranes. The features of the generated and real stained images, obtained through the Tile Encoder, are also used to calculate feature distillation loss, enhancing the model's feature extraction capabilities without increasing the inference burden. Additionally, CCPL performs statistical analysis on the focal optical density maps of both single channels to ensure consistency in staining distribution and intensity. Experimental results, based on quantitative metrics such as PSNR, SSIM, PCC, and FID, along with professional evaluations from pathologists, demonstrate that CCPL effectively preserves pathological features, generates high-quality virtual stained images, and provides robust support for automated pathological diagnosis using multimedia medical data.

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