论文标题
使用结构MRI图像在阿尔茨海默氏病中进行渐进式MCI分类的多流卷积神经网络
A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images
论文作者
论文摘要
阿尔茨海默氏病及其前驱阶段的早期诊断（也称为轻度认知障碍（MCI））至关重要，因为一些进行性MCI的患者会患上这种疾病。我们提出了一个具有基于斑块的成像数据的多流深卷积神经网络，以对稳定的MCI和渐进式MCI进行分类。首先，我们将阿尔茨海默氏病的MRI图像与认知正常受试者进行比较，以使用多元统计检验来识别不同的解剖标志。然后，这些地标用于提取被馈入建议的多流卷积神经网络的斑块以对MRI图像进行分类。接下来，我们使用阿尔茨海默氏病图像中的样本在不同的情况下训练建筑，这些样本在解剖学上与渐进式MCI相似，并且在认知上正常图像，以弥补缺乏渐进的MCI训练数据。最后，我们将经过训练的模型权重转移到提出的体系结构中，以便使用渐进的MCI和稳定的MCI数据微调模型。 ADNI-1数据集的实验结果表明，我们的方法的表现优于MCI分类的现有方法，F1得分为85.96％。
Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.
