A Sparse Bayesian Learning Algorithm for Longitudinal Image Data.
A Sparse Bayesian Learning Algorithm for Longitudinal Image Data.
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DOI：
10.1007/978-3-319-24574-4_49
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发表时间：
2015-10
期刊：
影响因子：
--
通讯作者：
Sabuncu MR
中科院分区：
文献类型：
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作者：
Sabuncu MR
纵向成像研究，即收集每个个体的连续（多次）扫描，正变得越来越普遍。机器学习领域通常忽略了纵向设计，因为许多算法是建立在假设每个数据点是一个独立样本的基础上的。因此，将通用机器学习工具应用于纵向图像数据可能不是最优的。在这里，我们提出了一种新的机器学习算法，旨在处理纵向图像数据集。我们的方法建立在基于稀疏贝叶斯图像的预测算法之上。我们的实证结果表明，该方法可以显著提高纵向临床数据的预测性能。
Longitudinal imaging studies, where serial (multiple) scans are collected on each individual, are becoming increasingly widespread. The field of machine learning has in general neglected the longitudinal design, since many algorithms are built on the assumption that each datapoint is an independent sample. Thus, the application of general purpose machine learning tools to longitudinal image data can be sub-optimal. Here, we present a novel machine learning algorithm designed to handle longitudinal image datasets. Our approach builds on a sparse Bayesian image-based prediction algorithm. Our empirical results demonstrate that the proposed method can offer a significant boost in prediction performance with longitudinal clinical data.