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English(EN) Concept-based explanation of gene expression prediction from H&E images

新框架将组织形态学与基因表达预测联系起来

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的可解释框架，使用视觉Transformer (ViT) 模型将转录程序与组织形态学联系起来。该框架结合了相关性传播和概念发现，提供了关于形态学模式如何影响基因表达预测的局部和全局见解。应用于结直肠癌数据，该方法准确预测了临床相关特征和分子表型，展示了亚型间的空间异质性并区分了患者预后。 AI

影响 增强了病理学中AI模型的可解释性，可能提高诊断准确性和对疾病机制的理解。

排序理由 学术论文，详细介绍了病理学模型可解释性的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

新框架将组织形态学与基因表达预测联系起来

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学术论文，详细介绍了病理学模型可解释性的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    基于概念的HE图像基因表达预测解释

    Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&amp;E images. However, existing explainability methods for vision transformer (ViT)-based models are largely limited to local heatmaps and do not reveal …

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