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English(EN) Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

贝叶斯神经网络预测肺肿瘤生长并进行不确定性评估

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种贝叶斯物理信息神经网络，用于从稀疏的CT扫描数据中预测肺肿瘤的生长。该模型集成了Gompertz生长动力学和贝叶斯推断，采用两阶段方法进行估计。在国家肺癌筛查试验（National Lung Screening Trial）的数据上进行评估，该框架展示了准确的预测能力，并提供了校准的不确定性估计，优于确定性方法。 AI

影响 这项研究为不确定性感知的医学预后提供了一种新颖的方法，有望在患者数据有限的情况下改善治疗规划。

排序理由 该集群包含一篇详细介绍医学数据新建模方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

贝叶斯神经网络预测肺肿瘤生长并进行不确定性评估

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该集群包含一篇详细介绍医学数据新建模方法的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Haoran Ma ·

    基于贝叶斯物理信息神经网络的稀疏纵向CT数据肺肿瘤生长不确定性感知预测

    This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability. A Bayesian physics-informed neural network is developed by combining Gompertz growth dynamics with low-dimensional Bayesian in…

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

    基于贝叶斯物理信息神经网络的稀疏纵向CT数据肺肿瘤生长不确定性感知预测

    This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability. A Bayesian physics-informed neural network is developed by combining Gompertz growth dynamics with low-dimensional Bayesian in…

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