用双曲随机编码预测未来认知能力下降
Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding
摘要：
双曲几何已经成功地应用于具有一般拓扑结构的大脑皮层和皮层下表面的建模。然而，类似于其他基于表面的脑形态分析方法，这种方法通常生成高维特征。它限制了他们在认知衰退预测研究中的统计能力，特别是在受试者数量有限的数据集中。为了解决上述限制，我们提出了一个新的框架称为双曲随机编码（HSC）。我们的初步实验结果表明，我们的算法在各种分类任务上取得了优异的成绩。我们的工作可能会丰富基于表面的脑成像研究工具，并可能导致诊断和预后指标，可用于个性化治疗策略。
英文摘要：
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However such approaches, similar to other surface based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
