提出可解释的全切片图像分类新模型，提升诊断准确率与可读性。
MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification
- 基于流形约束自适应聚类，端到端优化特征表示
- 在多中心数据集上实现更高分级准确率与可解释性
- 适合病理诊断辅助与医学AI可解释性研究者
全切片图像（WSI）是病理诊断与分型的金标准。现有主流两步框架采用离线训练的特征编码器，缺乏领域知识。基于注意力的多实例学习（MIL）方法结果导向，可解释性差；聚类方法虽能提供可解释过程，但面临高维特征与语义模糊的中心点问题。为此，我们提出一种端到端的MIL框架，融合格拉斯曼重嵌入与流形自适应聚类，利用流形几何结构提升聚类鲁棒性。同时设计先验知识引导的代理实例标签生成与聚合策略，近似局部标签并聚焦病理性肿瘤区域。在多中心WSI数据集上的实验表明：1）集成聚类的模型在分级准确率与可解释性上均表现更优；2）端到端学习获得更优特征表示，且计算开销可控。
原文摘要 · Abstract (English)
Whole slide images (WSIs) are the gold standard for pathological diagnosis and sub-typing. Current main-stream two-step frameworks employ offline feature encoders trained without domain-specific knowledge. Among them, attention-based multiple instance learning (MIL) methods are outcome-oriented and offer limited interpretability. Clustering-based approaches can provide explainable decision-making process but suffer from high dimension features and semantically ambiguous centroids. To this end, we propose an end-to-end MIL framework that integrates Grassmann re-embedding and manifold adaptive clustering, where the manifold geometric structure facilitates robust clustering results. Furthermore, we design a prior knowledge guiding proxy instance labeling and aggregation strategy to approximate patch labels and focus on pathologically relevant tumor regions. Experiments on multicentre WSI datasets demonstrate that: 1) our cluster-incorporated model achieves superior performance in both grading accuracy and interpretability; 2) end-to-end learning refines better feature representations and it requires acceptable computation resources.
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