Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia.
Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia.
复制标题
量化多个数据集在融合中的相互作用和贡献：应用精神分裂症的检测。
DOI：
10.1109/tmi.2017.2678483
复制
发表时间：
2017-07
影响因子：
10.6
通讯作者：
Adali T
中科院分区：
文献类型：
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作者：
Levin-Schwartz Y;Calhoun VD;Adali T
从多组数据中提取信息是许多学科固有的问题。这可以通过联合分析数据集（如数据融合）或单独分析然后组合（如数据集成）来实现。然而，选择最优的方法来组合和分析多集数据一直是一个挑战。这样做的主要原因是很难确定每个数据集对分析的最佳贡献，以及数据集之间潜在可利用的补充信息的数量。在本文中，我们提出了一种新的基于分类率的技术，以明确量化每个数据集对融合结果的贡献，并促进对真实数据的融合方法的直接比较，并将一种新的方法，独立向量分析（IVA）应用于多集融合。这种基于分类率的技术用于121名精神分裂症患者和150名健康对照者在执行三个任务期间收集的功能磁共振成像数据。通过这个应用程序，我们发现，虽然通过利用所有任务来实现最佳性能，但每个任务对结果的贡献并不相同，这个框架可以有效地量化每个任务的附加价值。我们的研究结果还表明，数据融合方法比数据集成方法更强大，前者实现了73.5%的分类率，后者实现了70.9%的分类率，当所有三个任务一起分析时，我们发现这种差异是显著的。最后，我们证明了IVA由于其灵活性，与流行的数据融合方法联合独立分量分析相比具有相当或更好的性能。
The extraction of information from multiple sets of data is a problem inherent to many disciplines. This is possible by either analyzing the datasets jointly as in data fusion, or separately and then combining, as in data integration. However, selecting the optimal method to combine and analyze multiset data is an ever-present challenge. The primary reason for this is the difficulty in determining the optimal contribution of each dataset to an analysis as well as the amount of potentially exploitable complementary information among datasets. In this paper, we propose a novel classification rate based technique to unambiguously quantify the contribution of each dataset to a fusion result as well as facilitate direct comparisons of fusion methods on real data and apply a new method, independent vector analysis (IVA), to multiset fusion. This classification rate based technique is used on functional magnetic resonance imaging data collected from 121 patients with schizophrenia and 150 healthy controls during the performance of three tasks. Through this application, we find that though optimal performance is achieved by exploiting all tasks, each task does not contribute equally to the result and this framework enables effective quantification of the value added by each task. Our results also demonstrate that data fusion methods are more powerful than data integration methods, with the former achieving a classification rate of 73.5 percent and the latter achieving one of 70.9 percent, a difference which we show is significant, when all three tasks are analyzed together. Finally, we show that IVA, due to its flexibility, has equivalent or superior performance compared with the popular data fusion method joint independent component analysis.