CLEAR-HPV: 全切片组织学中人乳头瘤病毒相关形态的可解释概念发现
CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology
AI总结：
提出CLEAR-HPV框架，通过注意力机制重构MIL潜在空间以自动发现角化、基底样和间质形态概念，生成空间概念图并用紧凑的概念分数向量实现可解释预测。
摘要：
人乳头瘤病毒（HPV）状态是头颈癌和宫颈癌预后及治疗反应的关键决定因素。尽管基于注意力的多实例学习（MIL）在HPV相关的全切片组织病理学中实现了强切片级预测，但其形态学可解释性有限。为解决这一局限，我们引入了CLEAR-HPV（Concept-Level Explainable Attention-guided Representation for HPV），该框架利用注意力重构MIL潜在空间，从而在训练过程中无需概念标签即可实现概念发现。在注意力加权的潜在空间中运行，CLEAR-HPV自动发现角化、基底样和间质形态概念，生成空间概念图，并使用紧凑的概念分数向量表示每个切片。CLEAR-HPV的概念分数向量保留了原始MIL嵌入的预测信息，同时将高维特征空间（例如1536维）减少到仅10个可解释概念。CLEAR-HPV在TCGA-HNSCC、TCGA-CESC和CPTAC-HNSCC上一致地泛化，通过一个通用的、与骨干网络无关的框架，为基于注意力的全切片组织病理学MIL模型提供紧凑的概念级可解释性。
英文摘要：
Human papillomavirus (HPV) status is a critical determinant of prognosis and treatment response in head and neck and cervical cancers. Although attention-based multiple instance learning (MIL) achieves strong slide-level prediction for HPV-related whole-slide histopathology, it provides limited morphologic interpretability. To address this limitation, we introduce Concept-Level Explainable Attention-guided Representation for HPV (CLEAR-HPV), a framework that restructures the MIL latent space using attention to enable concept discovery without requiring concept labels during training. Operating in an attention-weighted latent space, CLEAR-HPV automatically discovers keratinizing, basaloid, and stromal morphologic concepts, generates spatial concept maps, and represents each slide using a compact concept-fraction vector. CLEAR-HPV's concept-fraction vectors preserve the predictive information of the original MIL embeddings while reducing the high-dimensional feature space (e.g., 1536 dimensions) to only 10 interpretable concepts. CLEAR-HPV generalizes consistently across TCGA-HNSCC, TCGA-CESC, and CPTAC-HNSCC, providing compact, concept-level interpretability through a general, backbone-agnostic framework for attention-based MIL models of whole-slide histopathology.
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。
