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English(EN) DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction

新的DPsurv网络增强了可解释的癌症生存预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DPsurv，一种用于病理全切片图像生存预测的新型网络。该方法解决了现有方法中常见的可解释性和预测不确定性方面的局限性。DPsurv采用双原型证据融合技术，提供不确定性感知的生存区间，并通过斑块原型分配图和组件原型提供可解释性。在五个数据集上的实验表明，DPsurv在一致性指数和集成Brier分数方面表现优越，突显了其可靠性和透明度。 AI

影响 引入了一种更具可解释性和不确定性感知能力的病理生存预测方法，有望提高诊断可靠性。

排序理由 该集群包含一篇详细介绍使用全切片图像进行生存预测的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的DPsurv网络增强了可解释的癌症生存预测

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该集群包含一篇详细介绍使用全切片图像进行生存预测的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong, Jiangdong Qiu, Pei Liu, Kai He, Huazhu Fu, Mengling Feng ·

    DPsurv：用于不确定性感知和可解释全切片图像生存预测的双原型证据融合

    arXiv:2510.00053v2 Announce Type: replace-cross Abstract: Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prog…

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