论文标题
降低fMRI扫描的代表性学习方法的比较，用于分类多动症
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD
论文作者
论文摘要
本文比较了用于表示静止状态功能磁共振（fMRI）扫描的三种特征表示技术。提出的特征表示模型将平均fMRI扫描的时间视为图像数据的原始表示。通过使用这些特征将注意力缺陷多动障碍（ADHD）患者分类来评估表示形式的有效性。用于特征表示的维度还原方法是最大变化，局部线性嵌入和自动编码器。分类目的测试的分类器是神经网和支持向量机。使用具有四个隐藏层的自动编码器以及支持向量机分类器的分类精度为61.25％，灵敏度为65.69％，特异性为52.20％。
This paper compares three feature representation techniques used to represent resting state functional magnetic resonance (fMRI) scans. The proposed models of feature representation consider the time averaged fMRI scans as raw representation of image data. The effectiveness of the representation is evaluated by using these features for classification of Attention Deficit Hyperactivity Disorder (ADHD) patients from healthy controls. The dimensionality reduction methods used for feature representation are maximum-variance unfolding, locally linear embedding and auto-encoders. The classifiers tested for classification purpose were neural net and support vector machine. Using auto-encoders with four hidden layers along with a support vector machine classifier yielded a classification accuracy of 61.25% along with 65.69% sensitivity and 52.20% specificity.
