Separating the contributions of individual chromogenic stains in RGB histology whole slide images (WSIs) is essential for stain normalization, quantitative assessment of marker expression, and cell-level readouts in immunohistochemistry (IHC). Classical Beer-Lambert (BL) color deconvolution is well-established for two- or three-stain settings, but becomes under-determined and unstable for multiplex IHC (mIHC) with K>3 chromogens. We present a simple, data-driven encoder-decoder architecture that learns cohort-specific stain characteristics for mIHC RGB WSIs and yields crisp, well-separated per-stain concentration maps. The encoder is a compact U-Net that predicts K nonnegative concentration channels; the decoder is a differentiable BL forward model with a learnable stain matrix initialized from typical chromogen hues. Training is unsupervised with a perceptual reconstruction objective augmented by loss terms that discourage unnecessary stain mixing. On a colorectal mIHC panel comprising 5 stains (H, CDX2, MUC2, MUC5, CD8) we show excellent RGB reconstruction, and significantly reduced inter-channel bleed-through compared with matrix-based deconvolution. Code and model are available at https://github.com/measty/StainQuant.git.

翻译：在RGB组织学全切片图像中分离各色原染色的贡献，对于免疫组化中的染色标准化、标记物表达的定量评估以及细胞水平读数至关重要。经典的啤酒-朗伯颜色解卷积方法在双染色或三染色场景中已较为成熟，但对于色原数量K>3的多重免疫组化，该方法会变得欠定且不稳定。我们提出一种简单、数据驱动的编码器-解码器架构，该架构能够学习针对mIHC RGB WSIs的队列特异性染色特征，并生成清晰、分离良好的各染色浓度图。编码器采用紧凑的U-Net结构，用于预测K个非负浓度通道；解码器则是一个可微分的BL前向模型，其可学习的染色矩阵以典型色原色调初始化。训练过程采用无监督方式，以感知重建目标为基础，并通过添加抑制不必要染色混合的损失项进行增强。在一个包含5种染色（H、CDX2、MUC2、MUC5、CD8）的结直肠mIHC样本上，我们的方法展现出优异的RGB重建效果，并且相较于基于矩阵的解卷积方法，显著降低了通道间的串扰。代码与模型可在https://github.com/measty/StainQuant.git获取。