Cross-channel Perception Learning for H&E-to-IHC Virtual Staining
Journal:
arXiv
Published Date:
Jun 9, 2025
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.
