整合病理学基础模型与空间转录组学实现组织学图像的细胞分解
Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
AI总结：
提出一种轻量级方法，利用病理学基础模型特征和MLP回归器，从H&E图像直接预测细胞组成，性能媲美Hist2Cell且计算成本更低。
摘要：
数字病理学和现代深度学习的快速发展促进了病理学基础模型的出现，这些模型有望在统一模型中解决各种疾病条件下的通用病理学问题，无论是否进行微调。与此同时，空间转录组学已成为一种变革性技术，能够在苏木精-伊红（H&E）染色的组织学图像上分析基因表达。空间转录组学为在更精细的细胞水平上深入探索现有组织学图像提供了前所未有的机会。在这项工作中，我们提出了一种轻量级且训练高效的方法，通过利用从预训练的病理学基础模型中提取的信息丰富的特征嵌入，直接从H&E染色的组织学图像预测细胞组成。通过在由cell2location导出的细胞类型丰度上训练轻量级多层感知器（MLP）回归器，我们的方法有效地从病理学基础模型中蒸馏知识，并展示了无需实际执行昂贵的空间转录组学即可从组织学图像准确预测细胞类型组成的能力。与现有方法（如Hist2Cell）相比，我们的方法表现出具有竞争力的性能，同时显著降低了计算复杂度。
英文摘要：
The rapid development of digital pathology and modern deep learning has facilitated the emergence of pathology foundation models that are expected to solve general pathology problems under various disease conditions in one unified model, with or without fine-tuning. In parallel, spatial transcriptomics has emerged as a transformative technology that enables the profiling of gene expression on hematoxylin and eosin (H&E) stained histology images. Spatial transcriptomics unlocks the unprecedented opportunity to dive into existing histology images at a more granular, cellular level. In this work, we propose a lightweight and training-efficient approach to predict cellular composition directly from H&E-stained histology images by leveraging information-enriched feature embeddings extracted from pre-trained pathology foundation models. By training a lightweight multi-layer perceptron (MLP) regressor on cell-type abundances derived via cell2location, our method efficiently distills knowledge from pathology foundation models and demonstrates the ability to accurately predict cell-type compositions from histology images, without physically performing the costly spatial transcriptomics. Our method demonstrates competitive performance compared to existing methods such as Hist2Cell, while significantly reducing computational complexity.
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