解决病理图像中肿瘤与非肿瘤混淆问题，提升诊断可解释性。
Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition
- 用低秩约束子空间聚类分离非肿瘤实例，缓解样本不均衡问题。
- 通过对比学习构建原型，区分高度相似的肿瘤与癌前病变组织。
- 适合病理诊断辅助系统，尤其关注模型可解释性的研究者。
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，且全切片图像（WSIs）中非肿瘤实例数量远超肿瘤实例，导致多实例学习框架中实例-语义纠缠，降低模型表征能力与可解释性。为此，本文提出端到端的原型实例语义解耦框架PID-LRSC，从两方面解决：首先，通过次级实例子空间学习构建低秩正则化子空间聚类（LRSC），缓解因非肿瘤实例过多引发的实例纠缠；其次，采用增强对比学习设计原型实例语义解耦（PID），解决肿瘤与癌前病变组织高度相似带来的语义纠缠。在多中心病理数据集上开展大量实验表明，PID-LRSC优于其他SOTA方法。整体上，该框架在决策过程中提供更清晰的实例语义，显著提升辅助诊断结果的可靠性。
原文摘要 · Abstract (English)
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.
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