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A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR

基于深度学习的 FLAIR 皮质下血管性认知障碍不同亚型分类模型

  • 影响因子:3.99
  • DOI:10.3389/fnins.2020.00557
  • 作者列表:"Qi Chen","Yao Wang","Yage Qiu","Xiaowei Wu","Yan Zhou","Guangtao Zhai
  • 发表时间:2020-06-18
Abstract

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.

摘要

深度学习方法已经显示出其从图像中提取高级特征的巨大能力,并最近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和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,我们提出了一种新颖的端到端三维 (3D) 基于注意的残差神经网络 (ResNet) 架构用单次激发 T2-weighted 液体衰减反转恢复 (FLAIR) 序列对皮质下血管性认知障碍 (SVCI) 的不同亚型进行分类。我们的目的是开发一种卷积神经网络,为帮助医生诊断和早期治疗不同亚型的 SVCI 提供一种方便有效的方法。本文的实验数据来自仁济医院神经内科的 242 例患者,其中遗忘型轻度认知障碍 (a-MCI) 78 例,非遗忘型 MCI (na-MCI) 70 例,94 无认知功能障碍 (NCI)。我们提出的模型在训练集上的准确性达到了 98.6%,在验证集上的准确性达到了 97.3%。在未训练的测试集上的测试精度达到 93.8%,具有鲁棒性。我们提出的方法可为医生提供方便有效的辅助诊断和早期治疗方法。

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影响因子:2.57
发表时间:2020-01-06
DOI:10.1007/s12031-019-01474-x
作者列表:["Cheng, Xiao","Yang, Ying-Lin","Li, Wei-Han","Liu, Man","Zhang, Shan-Shan","Wang, Yue-Hua","Du, Guan-Hua"]

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影响因子:2.75
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DOI:10.1007/s00702-019-02124-7
作者列表:["Wang, Xiaodong","Shi, Cunxian","Pan, Hongxia","Meng, Xiaowen","Ji, Fuhai"]

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