Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Comput Methods Programs Biomed. 2022 Jan;213:106503. doi: 10.1016/j.cmpb.2021.106503. Epub 2021 Nov 6.
Alzheimer's disease (AD) is a fatal neurodegenerative disease. Predicting Mini-mental state examination (MMSE) based on magnetic resonance imaging (MRI) plays an important role in monitoring the progress of AD. Existing machine learning based methods cast MMSE prediction as a single metric regression problem simply and ignore the relationship between subjects with various scores.
In this study, we proposed a ranking convolutional neural network (rankCNN) to address the prediction of MMSE through muti-classification. Specifically, we use a 3D convolutional neural network with sharing weights to extract the feature from MRI, followed by multiple sub-networks which transform the cognitive regression into a series of simpler binary classification. In addition, we further use a ranking layer to measure the ranking information between samples to strengthen the ability of the classification by extracting more discriminative features.
We evaluated the proposed model on ADNI-1 and ADNI-2 datasets with a total of 1,569 subjects. The Root Mean Squared Error (RMSE) of our proposed model at baseline is 2.238 and 2.434 on ADNI-1 and ADNI-2, respectively. Extensive experimental results on ADNI-1 and ADNI-2 datasets demonstrate that our proposed model is superior to several state-of-the-art methods at both baseline and future MMSE prediction of subjects.
This paper provides a new method that can effectively predict the MMSE at baseline and future time points using baseline MRI, making it possible to use MRI for accurate early diagnosis of AD. The source code is freely available at https://github.com/fengduqianhe/ADrankCNN-master.
阿尔茨海默病（AD）是一种致命的神经退行性疾病。基于磁共振成像（MRI）预测简易精神状态检查（MMSE）在监测 AD 进展方面发挥着重要作用。现有的基于机器学习的方法简单地将 MMSE 预测视为单一指标回归问题，而忽略了具有不同分数的受试者之间的关系。
在这项研究中，我们提出了一种排序卷积神经网络（rankCNN），通过多分类来解决 MMSE 的预测问题。具体来说，我们使用具有共享权重的 3D 卷积神经网络从 MRI 中提取特征，然后是多个子网络，将认知回归转化为一系列更简单的二进制分类。此外，我们进一步使用排序层来衡量样本之间的排序信息，通过提取更多有区别的特征来增强分类能力。
我们在 ADNI-1 和 ADNI-2 数据集上评估了所提出的模型，共有 1569 名受试者。我们提出的模型在基线时的均方根误差（RMSE）在 ADNI-1 和 ADNI-2 上分别为 2.238 和 2.434。在 ADNI-1 和 ADNI-2 数据集上的广泛实验结果表明，我们提出的模型在基线和未来受试者的 MMSE 预测方面均优于几种最先进的方法。
本文提供了一种新的方法，可以使用基线 MRI 有效地预测基线和未来时间点的 MMSE，从而有可能使用 MRI 对 AD 进行准确的早期诊断。源代码可在 https://github.com/fengduqianhe/ADrankCNN-master 上免费获得。
