病理基础模型在空间域理解中的基准测试
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
AI总结：
提出SpaPath-Bench基准，通过空间域识别任务评估病理基础模型在区分组织区域和捕获空间关系方面的表示能力。
摘要：
病理基础模型（PFMs）已成为从全切片图像（WSIs）中学习可迁移表示的核心方法，通常通过下游临床终点进行基准测试。虽然这种任务级评估不可或缺，但它们对表示本身编码了什么提供了有限的见解，特别是PFM嵌入是否能够区分有意义的组织区域并捕获其空间关系。我们提出了SpaPath-Bench，一个表示级基准，旨在诊断PFMs中的空间表示能力。SpaPath-Bench将配对全切片图像和空间转录组学（ST）数据上的空间域识别（SDI）制定为诊断任务。它整理了42个公开的配对WSI和ST切片，支持跨19个编码器和7种SDI方法的大规模评估，并使用三个互补标准衡量分区质量：无监督空间一致性、转录组学参考一致性和专家参考一致性。在83K次运行中，SpaPath-Bench揭示了不同的预训练范式捕获了组织空间架构的不同方面，并为构建下一代空间感知计算病理模型提供了实用指导。代码和数据管道公开于https://bokai-zhao.github.io/SpaPath-benchboard/。
英文摘要：
Pathology foundation models (PFMs) have emerged as a core approach for learning transferable representations from whole slide images (WSIs), and they are typically benchmarked through downstream clinical endpoints. While such task level evaluations are indispensable, they offer limited insight into what the representations themselves encode, particularly whether PFM embeddings can distinguish meaningful tissue regions and capture their spatial relationships. We present SpaPath-Bench, a representation level benchmark designed to diagnose spatial representation capability in PFMs. SpaPath-Bench formulates spatial domain identification (SDI) on paired whole slide image and spatial transcriptomics (ST) data as a diagnostic task. It curates 42 public paired WSI and ST slides, enables large scale evaluation across 19 encoders and seven SDI methods, and measures partition quality using three complementary criteria: unsupervised spatial coherence, transcriptomics referenced agreement, and expert referenced agreement. Across 83K runs, SpaPath-Bench reveals that different pretraining paradigms capture distinct aspects of tissue spatial architecture, and it provides practical guidance for building the next generation of spatially aware computational pathology models. Code and data pipelines are publicly available at https://bokai-zhao.github.io/SpaPath-benchboard/.
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。
