基于信息驱动的病理基础模型融合以增强疾病表征
Information-driven Fusion of Pathology Foundation Models for Enhanced Disease Characterization
AI总结：
本研究提出基于信息驱动的病理基础模型融合方法，通过智能融合提升癌症分级和分期的预测性能与可解释性。
摘要：
基础模型（FMs）在多种病理任务中展现了强大的性能。尽管基础模型的预训练目标存在相似性，但对其互补性、嵌入空间中的冗余性或特征的生物解释仍缺乏深入理解。在本研究中，我们提出了一种信息驱动的智能融合策略，用于将多个病理基础模型整合到统一的表示中，并系统评估其在三种不同疾病中的癌症分级和分期性能。从肾（519张）、前列腺（490张）和直肠（200张）癌症的诊断H&E全切片图像中，将低级与高级或分期分为两类。考虑了 tile 级基础模型（Conch v1.5, MUSK, Virchow2, H-Optimus1, Prov-Gigapath）和 slide 级基础模型（TITAN, CHIEF, MADELEINE）来训练下游分类器。然后我们在 tile 和 slide 级别评估了三种 FM 融合方案：多数投票集成、朴素特征拼接以及基于相关性引导剪枝的智能融合。在患者分层交叉验证与留出测试中，tile 级嵌入的智能融合在三种癌症中均比最佳单个 FM 和朴素融合表现出一致的分类性能提升。全局相似性度量揭示了 FM 嵌入空间的显著对齐，与局部邻居一致性的较低形成对比，表明 FM 之间存在互补的细粒度信息。注意力图显示，智能融合在肿瘤区域集中了注意力，同时减少了对良性区域的错误关注。我们的发现表明，基于相关性的智能融合可以产生紧凑、任务定制的表示，从而在下游计算病理学任务中提升预测性能和可解释性。
英文摘要：
Foundation models (FMs) have demonstrated strong performance across diverse pathology tasks. While there are similarities in the pre-training objectives of FMs, there is still limited understanding of their complementarity, redundancy in embedding spaces, or biological interpretation of features. In this study, we propose an information-driven, intelligent fusion strategy for integrating multiple pathology FMs into a unified representation and systematically evaluate its performance for cancer grading and staging across three distinct diseases. Diagnostic H&E whole-slide images from kidney (519 slides), prostate (490 slides), and rectal (200 slides) cancers were dichotomized into low versus high grade or stage. Both tile-level FMs (Conch v1.5, MUSK, Virchow2, H-Optimus1, Prov-Gigapath) and slide-level FMs (TITAN, CHIEF, MADELEINE) were considered to train downstream classifiers. We then evaluated three FM fusion schemes at both tile and slide levels: majority-vote ensembling, naive feature concatenation, and intelligent fusion based on correlation-guided pruning of redundant features. Under patient-stratified cross-validation with hold-out testing, intelligent fusion of tile-level embeddings yielded consistent gains in classification performance across all three cancers compared with the best single FMs and naive fusion. Global similarity metrics revealed substantial alignment of FM embedding spaces, contrasted by lower local neighborhood agreement, indicating complementary fine-grained information across FMs. Attention maps showed that intelligent fusion yielded concentrated attention on tumor regions while reducing spurious focus on benign regions. Our findings suggest that intelligent, correlation-guided fusion of pathology FMs can yield compact, task-tailored representations that enhance both predictive performance and interpretability in downstream computational pathology tasks.
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