Multiple instance learning for classification of dementia in brain MRI
Multiple instance learning for classification of dementia in brain MRI
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DOI：
10.1016/j.media.2014.04.006
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发表时间：
2014-07-01
影响因子：
10.9
通讯作者：
Rueckert, Daniel
中科院分区：
文献类型：
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作者：
Tong, Tong;Wolz, Robin;Rueckert, Daniel
机器学习技术已被广泛应用于从结构脑磁共振成像数据中检测形态异常，并支持痴呆症等神经疾病的诊断。在本文中，我们提出使用多实例学习(MIL)方法来检测阿尔茨海默病(AD)及其前驱期轻度认知障碍(MCI)。在我们的工作中，局部强度块被提取作为特征。然而，并不是所有从痴呆症患者身上提取的贴片都同样受到这种疾病的影响，其中一些可能不具有与疾病相关的形态特征。因此，在给这些贴片分配疾病标签时存在一些模糊之处。训练标签模糊的问题可以通过弱监督学习技术(如MIL)来解决，该方法为每幅图像建立一个图来利用块之间的关系，从而解决MIL问题。所构造的图包含了斑块的外观以及它们之间的关系的信息，能够反映图像的内在结构，有助于分类。使用ADNI研究的834名受试者的基线MR图像，该方法在AD患者和健康对照组之间的分类准确率可以达到89%，在留一法交叉验证中被定义为稳定型MCI和进展型MCI的患者之间的分类准确率可以达到70%。与使用相同数据集的两种最新方法相比，所提出的方法可以获得相似或更好的结果，为神经退行性疾病的检测和预测提供了一种替代框架。(C)2014爱思唯尔B.V.保留所有权利。
Machine learning techniques have been widely used to detect morphological abnormalities from structural brain magnetic resonance imaging data and to support the diagnosis of neurological diseases such as dementia. In this paper, we propose to use a multiple instance learning (MIL) method in an application for the detection of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). In our work, local intensity patches are extracted as features. However, not all the patches extracted from patients with dementia are equally affected by the disease and some of them may not be characteristic of morphology associated with the disease. Therefore, there is some ambiguity in assigning disease labels to these patches. The problem of the ambiguous training labels can be addressed by weakly supervised learning techniques such as MIL A graph is built for each image to exploit the relationships among the patches and then to solve the MIL problem. The constructed graphs contain information about the appearances of patches and the relationships among them, which can reflect the inherent structures of images and aids the classification. Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 89% between AD patients and healthy controls, and 70% between patients defined as stable MCI and progressive MCI in a leave-one-out cross validation. Compared with two state-of-the-art methods using the same dataset, the proposed method can achieve similar or improved results, providing an alternative framework for the detection and prediction of neurodegenerative diseases. (C) 2014 Elsevier B.V. All rights reserved.