English(EN) Predicting Single-Cell Spatial Gene Expression from H&E using Pathology Foundation Models

病理基础模型从组织学图像预测基因表达

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种直接从H&E染色组织学图像预测单细胞空间基因表达的方法。通过调整病理基础模型，他们的方法与标准方法相比显著提高了预测准确性，达到了最先进的性能。研究强调，虽然基于形态学的推断存在局限性，但它可以成功恢复具有生物学意义的空间表达模式，特别是对于空间变异基因。 AI

影响这项研究展示了人工智能从常规医学影像中提取复杂生物学信息的潜力，有望加速分子发现。

排序理由这是一篇研究论文，详细介绍了一种使用AI模型从医学影像预测基因表达的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Ashley P. Tsang, Evan Liu, Conrad Foo, Bob Chen, Hassaan Maan, Donny Chan, Elisa Penna, Veronica Ibarra Lopez, Hector Corrada Bravo, Aicha BenTaieb, Lisa M. McGinnis, Runmin Wei, Kenneth T. Gao · 2026-11-11 05:00

利用病理基础模型从 H&E 预测单细胞空间基因表达

arXiv:2611.13649v1 Announce Type: new Abstract: Predicting gene expression directly from hematoxylin and eosin (H&E) images offers a scalable and cost-effective approach to deriving spatial molecular insights from routine histology. Pathology foundation models, typically pret…