用专家混合与生成模型提升病理切片分类精度
MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification
- 引入专家混合机制，智能筛选关键区域，过滤噪声
- 通过扩散生成过程直接输出类别分布，准确率领先
- 首个在病理切片分类中采用生成式方法的模型
全切片图像（WSI）分类因图像尺寸巨大和大量无信息区域导致特征聚合时噪声多、数据不平衡。为此，我们提出MExD——一种融合专家混合（MoE）机制与扩散模型的专家注入式生成模型。MExD通过新型MoE聚合器均衡块特征分布，选择性强化相关特征，有效过滤噪声、缓解数据不平衡并提取核心特征。这些特征经基于扩散的生成过程整合，直接生成WSI的类别分布。相比传统判别方法，MExD是首个在WSI分类中采用生成策略的模型，能捕捉细粒度信息，实现稳健精确的结果。我们在Camelyon16、TCGA-NSCLC和BRACS三个常用基准上验证，无论二分类还是多分类任务均持续达到当前最优性能。
原文摘要 · Abstract (English)
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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