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

新框架解释病理图像的基因表达预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新颖的框架，利用vision transformers来解释HE染色病理图像的基因表达预测。该框架结合了相关性传播和概念发现，将形态学特征与转录组程序联系起来，提供局部和全局的见解。该方法应用于结直肠癌数据，可准确预测临床相关特征和分子表型，展示了其在更广泛的ViT基础病理模型中的潜力。 AI

影响 增强了医学影像中AI模型的可解释性，可能提高诊断准确性和信任度。

排序理由 该项目是一篇研究论文，详细介绍了在特定领域解释AI模型预测的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

新框架解释病理图像的基因表达预测

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该项目是一篇研究论文，详细介绍了在特定领域解释AI模型预测的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Amos Muench, Jonathan Thielmann, Reduan Achtibat, Maximilian Dreyer, Philip Bischoff, Caroline Forsythe, Hamidreza Parand, Thomas Walter, David Horst, Sebastian Lapuschkin, Wojciech Samek, Teresa Gabriela Krieger ·

    基于概念的HE图像基因表达预测解释

    arXiv:2608.16669v1 Announce Type: new Abstract: 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 larg…

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