Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.
Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier.
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DOI：
10.1016/j.neuroimage.2011.06.029
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发表时间：
2011-10-01
期刊：
影响因子：
5.7
通讯作者：
Kloeppel, Stefan
中科院分区：
文献类型：
--
作者：
Abdulkadir, Ahmed;Mortamet, Benedicte;Vemuri, Prashanthi;Jack, Clifford R., Jr.;Krueger, Gunnar;Kloeppel, Stefan
基于结构磁共振成像（MRI）数据的全自动机器学习方法可以帮助放射科医生诊断阿尔茨海默病（AD）。这些算法需要大量的数据集来学习分离有和没有AD的受试者。训练和测试数据可能来自异构硬件设置，这可能会影响疾病分类的性能。来自多中心阿尔茨海默病神经成像倡议（ADNI）的226名健康对照者和191名可能患有AD的个体的总共518次MRI会话被用于调查是否通过采集硬件分组数据。（即，供应商、场强、线圈系统）对于支持向量机（SVM）分类器的性能是有益的，与来自不同硬件的数据混合的情况相比。我们比较了（a）硬件变化对疾病影响和（B）在同一台机器上重新扫描同一受试者所产生的变化导致的SVM决策值的变化。最大的准确率为87%，获得了所有417个科目的训练集。在每个诊断组中用95名受试者训练并使用异构扫描仪设置采集的分类器在相同大小的独立集上测试时，经验检测准确率为84.2±2.4%。这些结果反映了最近研究报告的准确性。令人鼓舞的是，在使用同质和异构硬件设置获取的图像上训练的分类器具有等效的交叉验证性能。在同一台机器上获得的同一受试者的两次扫描具有非常相似的决策值，通常被归类为同一组。当在两台具有不同场强的扫描仪上对同一受试者进行两次采集时，会引入更高的变化。两个诊断组的变异无偏且相似。该研究的结果鼓励汇集来自不同站点的数据，以增加训练样本的数量，从而提高疾病分类器的性能。虽然很小，但硬件的变化可能导致决策值的变化，从而导致诊断分组的变化。这项研究的结果提供了一个自动化的疾病诊断方法，涉及扫描采集不同的硬件集的诊断准确性的估计。此外，我们表明，在性能估计的置信水平显着依赖于训练样本的大小，因此应考虑在临床环境中。
Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer’s disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of training samples and thereby improving performance of disease classifiers. Although small, a change in hardware could lead to a change of the decision value and thus diagnostic grouping. The findings of this study provide estimators for diagnostic accuracy of an automated disease diagnosis method involving scans acquired with different sets of hardware. Furthermore, we show that the level of confidence in the performance estimation significantly depends on the size of the training sample, and hence should be taken into account in a clinical setting.
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影响因子：
5.7
作者：
Stonnington CM;Tan G;Klöppel S;Chu C;Draganski B;Jack CR Jr;Chen K;Ashburner J;Frackowiak RS
通讯作者：
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