The tumor region plays a key role in pathological diagnosis. Tumor tissues are highly similar to precancerous lesions and non tumor instances often greatly exceed tumor instances in whole slide images (WSIs). These issues cause instance-semantic entanglement in multi-instance learning frameworks, degrading both model representation capability and interpretability. To address this, we propose an end-to-end prototype instance semantic disentanglement framework with low-rank regularized subspace clustering, PID-LRSC, in two aspects. First, we use secondary instance subspace learning to construct low-rank regularized subspace clustering (LRSC), addressing instance entanglement caused by an excessive proportion of non tumor instances. Second, we employ enhanced contrastive learning to design prototype instance semantic disentanglement (PID), resolving semantic entanglement caused by the high similarity between tumor and precancerous tissues. We conduct extensive experiments on multicentre pathology datasets, implying that PID-LRSC outperforms other SOTA methods. Overall, PID-LRSC provides clearer instance semantics during decision-making and significantly enhances the reliability of auxiliary diagnostic outcomes.

翻译：肿瘤区域在病理诊断中起关键作用。肿瘤组织与癌前病变高度相似，且在全切片图像（WSIs）中非肿瘤实例数量常远超肿瘤实例。这些问题导致多示例学习框架中出现实例-语义纠缠，降低了模型的表示能力与可解释性。为此，我们提出一种端到端的原型实例语义解耦框架，结合低秩正则化子空间聚类（PID-LRSC），从两方面解决该问题：首先，通过二级实例子空间学习构建低秩正则化子空间聚类（LRSC），以解决因非肿瘤实例比例过高导致的实例纠缠；其次，采用增强对比学习设计原型实例语义解耦（PID），以解决肿瘤与癌前组织高度相似引发的语义纠缠。我们在多中心病理数据集上进行了广泛实验，结果表明PID-LRSC优于其他先进方法。总体而言，PID-LRSC在决策过程中提供了更清晰的实例语义，显著提升了辅助诊断结果的可靠性。