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

• URL: http://arxiv.org/abs/2503.12401v1
• Date: Sun, 16 Mar 2025 08:04:17 GMT
• Title: MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification
• Authors: Jianwei Zhao, Xin Li, Fan Yang, Qiang Zhai, Ao Luo, Yang Zhao, Hong Cheng, Huazhu Fu,
• Abstract summary: Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions.<n>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.
• Score: 46.89908887119571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative 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 diffusion-based 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-the-art performance in both binary and multi-class tasks.

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