MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification

Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous noninformative regions, which introduce noise and cause data imbalance during feature aggregation. To address these issues, we propose MExD, an Expert-Infused Diffusion Model that combines the strengths of a Mixture-of-Experts (MoE) mechanism with a diffusion model for enhanced classification. MExD balances patch feature distribution through a novel MoE-based aggregator that selectively emphasizes relevant information, effectively filtering noise, addressing data imbalance, and extracting essential features. These features are then integrated via a diffusionbased generative process to directly yield the class distribution for the WSI. Moving beyond conventional discriminative approaches, MExD represents the first generative strategy in WSI classification, capturing fine-grained details for robust and precise results. Our MExD is validated on three widely-used benchmarks-Camelyon16, TCGA-NSCLC, and BRACS-consistently achieving state-of-theart performance in both binary and multi-class tasks. Our code and model are available at https://github . com/JWZhao-uestc/MExD.