Liu Manhua, Zhang Daoqiang, Yap Pew-Thian, Shen Dinggang
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):239-47. doi: 10.1007/978-3-642-33454-2_30.
Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage--mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized LASSO.
基于机器学习技术的神经影像分析已被广泛用于辅助诊断诸如阿尔茨海默病及其前驱阶段——轻度认知障碍等脑部疾病。脑影像分析中的一个主要问题是从大量原始影像特征中学习最相关的特征，这些特征的数量远远超过训练样本。这使得疾病分类和解释任务极具挑战性。通过L1正则化进行的稀疏编码，如套索回归，可以提供一种有效的方法来选择最相关的特征，以减轻维度灾难并实现更准确的分类。然而，所选特征可能会随机分布在整个大脑中，尽管实际上疾病引起的异常变化通常发生在少数几个相邻区域。为了解决这个问题，我们研究了一种树引导的稀疏编码方法，以识别大脑区域中的分组影像特征，用于指导疾病分类和解释。在稀疏编码过程中，通过树引导正则化施加图像结构的空间关系。我们在ADNI数据集上的实验结果表明，与传统的L1正则化套索回归相比，树引导的稀疏编码方法不仅能实现更好的分类准确率，还能对脑部疾病进行更有意义的诊断。
