VQ-CSDN: A Vector Quantized Content-Stain Decoupling Network for Stain Normalization of H&E Histopathology Images

Stain normalization is a fundamental preprocessing technique in digital and computational pathology, and it is important for improving the consistency of clinical diagnosis by reducing color heterogeneity. However, existing stain normalization methods are often limited by content-stain entanglement and an ambiguously modeled content-stain relationship, resulting in unstable structural preservation, cross-center adaptation burden, and sensitivity to template selection. To address these limitations, we propose a Vector-Quantized Content-Stain Decoupling Network (VQ-CSDN) that decouples structure-defining content from stain appearance and learns a center-specific content-to-stain mapping, enabling structure-preserving, template-free stain transfer from unseen source centers without source-specific retraining. VQ-CSDN employs two independent encoders to extract content and stain features respectively and a generator for reconstruction. To better characterize cross-center content feature spaces and capture staining styles, we introduce vector quantization with a global content codebook shared across centers and center-specific stain codebooks. Moreover, a content-to-stain module directly infers stain features from the content feature and a target staining style, enabling end-to-end stain transfer without template selection. To further promote structural fidelity and enhance staining style separability, we propose center-specific gradient-driven discriminators that jointly assess center-level stain conformity and the morphological consistency of generated images. Extensive experiments on multiple public and private pathology datasets show that VQ-CSDN consistently enhances normalization quality, reflected by increased SSIM/PSNR and decreased FID, and further improves downstream benign-malignant classification performance (AUC 0.9738) over state-of-the-art methods.