流体衰减反转恢复
认知障碍
神经科学
人工智能
深度学习
认知
磁共振成像
医学
心理学
计算机科学
放射科
作者
Qi Chen,Yao Wang,Yage Qiu,Xiaowei Wu,Yan Zhou,Guangtao Zhai
标识
DOI:10.3389/fnins.2020.00557
摘要
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 3D attention-based Resnet network architecture to classify different subtypes of Subcortical Vascular Cognitive Impairment (SVCI) with single T2-weighted FLAIR sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in 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 a-MCI, 70 na-MCI and 94 NCI. The accuracy of our proposed model has reached 98.6% on training set and 97.3% on validation set. The test accuracy on untrained testing set reaches 93.8% with robustness. Our proposed method can provides a convenient and effective way to assist doctors in diagnosis and early treatment.
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