原型实例语义解耦与低秩正则化子空间聚类用于WSI可解释识别
Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition
AI总结：
本文提出PID-LRSC框架，通过低秩正则化子空间聚类和增强对比学习解决WSI中实例与语义耦合问题，提升模型可解释性和诊断可靠性。
摘要：
肿瘤区域在病理诊断中起关键作用。肿瘤组织高度相似于癌前病变，而非肿瘤实例在整张滑动图像（WSI）中往往远多于肿瘤实例。这些问题导致多实例学习框架中出现实例-语义耦合，降低模型表示能力和可解释性。为此，我们提出一个端到端的原型实例语义解耦框架，即低秩正则化子空间聚类（PID-LRSC），从两个方面进行改进。首先，我们使用二次实例子空间学习构建低秩正则化子空间聚类（LRSC），解决由非肿瘤实例比例过高导致的实例耦合问题。其次，我们采用增强对比学习设计原型实例语义解耦（PID），解决由肿瘤和癌前组织高度相似性导致的语义耦合问题。我们在多中心病理数据集上进行了广泛实验，表明PID-LRSC优于其他SOTA方法。总体而言，PID-LRSC在决策过程中提供更清晰的实例语义，并显著提高了辅助诊断结果的可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
