Deep ensemble learning of sparse regression models for brain disease diagnosis.
Deep ensemble learning of sparse regression models for brain disease diagnosis.
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DOI：
10.1016/j.media.2017.01.008
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发表时间：
2017-04
影响因子：
10.9
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Suk HI;Lee SW;Shen D;Alzheimer’s Disease Neuroimaging Initiative
最近关于脑成像分析的研究证明了机器学习技术在计算机辅助脑部疾病诊断干预中的核心作用。在各种机器学习技术中，稀疏回归模型已经证明了它们在处理高维数据方面的有效性，但训练样本数量很少，特别是在医学问题上。与此同时，深度学习方法在各种应用中取得了巨大的成功，表现优于最先进的表现。在本文中，我们提出了一种新的框架，将稀疏回归和深度学习这两种概念上不同的方法结合起来，用于阿尔茨海默病/轻度认知障碍的诊断和预后。具体地说，我们首先训练多个稀疏回归模型，每个模型用不同的正则化控制参数值来训练。因此，我们的多重稀疏回归模型可能从原始特征集中选择不同的特征子集；因此，它们具有不同的能力来预测响应值，即在我们的工作中的临床标签和临床评分。通过将稀疏回归模型的响应值作为目标级表示，我们构建了一个用于临床决策的深度卷积神经网络，因此我们称之为深度集成稀疏回归网络。据我们所知，这是首次将稀疏回归模型与深度神经网络相结合的工作。在我们与ADNI队列的实验中，我们通过在三个分类任务中获得最高的诊断准确率来验证所提出的方法的有效性。我们还对我们的结果进行了严格的分析，并与以往文献中关于ADNI队列的研究进行了比较。
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.
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影响因子：
5.7
作者：
Liao, Shu;Jia, Hongjun;Wu, Guorong;Shen, Dinggang
通讯作者：
Shen, Dinggang
影响因子：
5.7
作者：
Cho Y;Seong JK;Jeong Y;Shin SY;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
10.6
作者：
Brosch, Tom;Tang, Lisa Y. W.;Tam, Roger
通讯作者：
Tam, Roger
影响因子：
14
作者：
通讯作者：
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DOI：
10.1007/978-3-319-10443-0_39
发表时间：
2014
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
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作者：
通讯作者：
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