提出新模型提升病理切片分析准确率与可解释性
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
- 用代理聚合+掩码去噪机制替代传统注意力
- 在多个数据集上实现最优分类性能，尤其擅长发现微转移灶
- 适合医学图像分析、弱监督学习研究者参考
病理切片分析是医学诊断的金标准。全切片图像（WSI）的吉字节级分辨率和缺乏细粒度标注使得直接分类与分析极具挑战。弱监督学习中的多实例学习（MIL）为WSI分类提供了可行方案，现有方法多依赖注意力机制衡量实例重要性，但其无法捕捉实例间关系，且自注意力带来二次计算复杂度。为此，我们提出AMD-MIL：一种带有掩码去噪机制的代理聚合模型。代理标记作为查询与键之间的中间变量，用于计算实例重要性；从代理聚合值映射出的掩码矩阵与去噪矩阵，动态屏蔽低贡献特征并消除噪声。该机制通过调整特征表示，实现更优注意力分配，有效捕捉癌症中的微转移灶，并提升模型可解释性。在CAMELYON-16、CAMELYON-17、TCGA-KIDNEY和TCGA-LUNG四个数据集上的大量实验表明，AMD-MIL显著优于当前最优方法。
原文摘要 · Abstract (English)
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. The gigapixel resolution of WSI and the absence of fine-grained annotations make direct classification and analysis challenging. In weakly supervised learning, multiple instance learning (MIL) presents a promising approach for WSI classification. The prevailing strategy is to use attention mechanisms to measure instance importance for classification. However, attention mechanisms fail to capture inter-instance information, and self-attention causes quadratic computational complexity. To address these challenges, we propose AMD-MIL, an agent aggregator with a mask denoise mechanism. The agent token acts as an intermediate variable between the query and key for computing instance importance. Mask and denoising matrices, mapped from agents-aggregated value, dynamically mask low-contribution representations and eliminate noise. AMD-MIL achieves better attention allocation by adjusting feature representations, capturing micro-metastases in cancer, and improving interpretability. Extensive experiments on CAMELYON-16, CAMELYON-17, TCGA-KIDNEY, and TCGA-LUNG show AMD-MIL's superiority over state-of-the-art methods.
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