Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.
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DOI：
10.1007/s00429-013-0687-3
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发表时间：
2015-03
影响因子：
3.1
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
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作者：
Suk HI;Lee SW;Shen D;Alzheimer’s Disease Neuroimaging Initiative
近年来，计算机辅助诊断阿尔茨海默病（AD）及其前驱阶段轻度认知障碍（MCI）引起了人们极大的兴趣。与之前考虑简单的低级特征（如MRI的灰质组织体积和PET的平均信号强度）的方法不同，在本文中，我们提出了一种基于深度学习的潜在特征表示，并使用堆叠自编码器（SAE）。我们认为在特征之间的关系等底层特征中存在着潜在的非线性复杂模式。将潜在信息与原始特征相结合，有助于建立AD/MCI分类的鲁棒模型，具有较高的诊断准确率。此外，由于深度学习预训练的无监督特性，我们可以利用目标无关样本来初始化SAE的参数，从而在与目标相关样本的微调中找到最优参数，并进一步提高四个二元分类问题的分类性能：AD与健康正常对照（HC）， MCI与HC， AD与MCI， MCI转换器（MCI- c）与MCI非转换器（MCI- nc）。在ADNI数据集上的实验中，我们验证了该方法的有效性，AD/HC、MCI/HC、AD/MCI和MCI- c /MCI- nc的分类准确率分别为98.8%、90.7%、83.7%和83.3%。我们相信深度学习可以为神经成像数据分析提供新的思路，我们的工作展示了这种方法在脑部疾病诊断中的适用性。
Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.