A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR
A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR
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DOI：
10.3389/fnins.2020.00557
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发表时间：
2020-06-18
影响因子：
4.3
通讯作者：
Zhai, Guangtao
中科院分区：
文献类型：
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作者：
Chen, Qi;Wang, Yao;Zhai, Guangtao
深度学习方法已经显示出从图像中提取高级特征的强大能力，最近已被用于有效的医学成像分类。然而，医学图像的训练样本受到患者数量以及医学伦理问题的限制，使得神经网络的训练变得困难。在本文中，我们提出了一种新的端到端的三维（3D）的注意力为基础的残差神经网络（ResNet）架构分类不同亚型的皮层下血管性认知障碍（SVCI）与单次拍摄T2加权液体衰减反转恢复（FLAIR）序列。我们的目标是开发一个卷积神经网络，以提供一个方便和有效的方法来帮助医生在不同亚型的SVCI的诊断和早期治疗。本文收集了242例来自仁济医院神经内科的患者的实验数据，其中遗忘型轻度认知损害（a-MCI）78例，非遗忘型轻度认知损害（na-MCI）70例，无认知损害（NCI）94例。我们所提出的模型的准确性已经达到98.6%的训练集和97.3%的验证集。在未经训练的测试集上，测试准确率达到93.8%，具有鲁棒性。我们所提出的方法可以提供一个方便和有效的方法，以协助医生的诊断和早期治疗。
Deep learning methods have shown their great capability of extracting high-level features from image and have been used for effective medical imaging classification recently. However, training samples of medical images are restricted by the amount of patients as well as medical ethics issues, making it hard to train the neural networks. In this paper, we propose a novel end-to-end three-dimensional (3D) attention-based residual neural network (ResNet) architecture to classify different subtypes of subcortical vascular cognitive impairment (SVCI) with single-shot T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in the diagnosis and early treatment of the different subtypes of SVCI. The experiment data in this paper are collected from 242 patients from the Neurology Department of Renji Hospital, including 78 amnestic mild cognitive impairment (a-MCI), 70 nonamnestic MCI (na-MCI), and 94 no cognitive impairment (NCI). The accuracy of our proposed model has reached 98.6% on a training set and 97.3% on a validation set. The test accuracy on an untrained testing set reaches 93.8% with robustness. Our proposed method can provide a convenient and effective way to assist doctors in the diagnosis and early treatment.