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1Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion

DUAN Yifan,WANG Yu,FU Changyang,XIAO Hongbing,XING Suxia

本研究提出一种基于结构和功能双模态磁共振成像数据融合的抑郁症分类算法,首先利用功能脑网络和深度学习网络分别提取功能和结构磁共振成像数据特征,并计算类概率,然后使用软投票法和加权投票法在决策层对两种类概率数据进行融合,充分提取功能与结构磁共振成像的数据信息,得到更加准确的分类效果.试验结果表明,数据融合方法可以显著提高抑郁症分类效果,获得91.34％的准确率和96.62％的召回率,更好地实现了抑郁症的辅助诊断与预后.

2022Chinese Journal of Medical Physics（2022）

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2AUXILIARY DIAGNOSIS OF ALZHEIMER'S DISEASE BASED ON FUSION OF FMRI AND SMRI FEATURES

Liu Xi,Wang Yu,Fu Changyang,Xiao Hongbing,Xing Suxia

阿尔茨海默病是老年人常见的一种慢性进行性神经退化疾病,提出一种融合功能磁共振成像和结构磁共振成像信息辅助诊断方法.采用时间窗、主成分分析和线性判别分析融合策略提取功能磁共振成像特征,采用基于支持向量机递归特征消除和线性判别分析提取结构磁共振成像特征,将两种模态的特征通过串行融合的方式转化为一个向量输入SVM分类器,并获得分类结果.在ADNI数据库中的实验验证,两种模态特征融合后AD/SMC、NC/SMC、AD/NC分类准确率分别为94.1％、95.5％和96％.

2022Computer Applications and Software（2022）

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3Major Depression Disorder Diagnosis and Analysis Based on Structural Magnetic Resonance Imaging and Deep Learning.

Yu Wang, Ning Gong,Changyang Fu

Major depression disorder is one of the diseases with the highest rate of disability and morbidity and is associated with numerous structural and functional differences in neural systems. However, it is difficult to analyze digital medical imaging data without computational intervention. A voxel-wise densely connected convolutional neural network, Three-dimensional Densenet (3D-DenseNet), is proposed to mine the feature differences. In addition, a novel transfer learning method, called Alzheimer's Disease Neuroimaging Initiative Transfer (ADNI-Transfer), is designed and combined with the proposed 3D-DenseNet. The experimental results on a database that contains 174 subjects, including 99 patients with major depression disorder and 75 healthy controls, show that large changes in brain structures between major depressive disorder patients and healthy controls mainly are located in the regions including superior frontal gyrus, dorsolateral, middle temporal gyrus, middle frontal gyrus, postcentral gyrus, inferior temporal gyrus. In addition, the proposed deep learning network can better extract different features of brain structures between major depressive disorder patients and healthy controls and achieve excellent classification results of major depressive disorder. At the same time, the designed transfer learning method can further improve classification performance. These results verify that our proposed method is feasible and valid for diagnosing and analyzing major depression disorder.

2021JOURNAL OF INTEGRATIVE NEUROSCIENCE（2021）引用：8

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4Assisted diagnosis of major depression disorder using deep learning and structural magnetic resonance imaging

王瑜,邢素霞,肖洪兵,付常洋

抑郁症是致残率和发病率最高的疾病之一,全球约有3亿人正遭受着抑郁症的困扰.然而,目前并没有有效的生物特征和临床方法能够帮助医生对抑郁症进行准确的诊断.针对此任务,本文将计算机视觉领域的前沿深度学习模型进行优化与适配,应用于抑郁症的辅助诊断,并在此基础上引入迁移学习,取得了很好的效果.实验结果表明,同前沿算法模型相比,本文提出的方法能够有效提高抑郁症与健康对照者的结构磁共振成像分类准确率和召回率,充分验证了提出方法的有效性和优越性.

2021CAAI Transactions on Intelligent Systems引用：4

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5Aided diagnosis of major depressive disorder based on canonical correlation analysis and bimodal data fusion

计亚荣,王瑜,邢素霞,肖洪兵,付常洋

为了充分提取抑郁症患者的磁共振影像信息,提高抑郁症的诊断准确率,本研究将功能磁共振图像与结构磁共振图像作为研究对象,提出一种双模态数据融合的抑郁症分类算法.首先构建4种不同尺度的功能脑网络,提取功能磁共振图像的数据特征,然后使用迁移学习处理的三维密集连接卷积神经网络,提取结构磁共振图像的数据特征,接着使用典型相关分析方法融合两种特征,最后使用支持向量机对融合特征进行分类,从而将受试者识别为健康者或抑郁症患者.实验结果表明,本文提出的方法可获得89.56％的分类准确率与95.48％的召回率,与单模态数据分类相比,基于双模态数据的分类方法具有更好的分类性能.此外,典型相关分析法可以有效融合双模态的图像特征.

2021Chinese Journal of Medical Physics（2021）

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6Classification of depression using fusion features based on multi-scale functional brain network

Changyang FU,Yu WANG,Hongbing XIAO,Suxia XING

提出一种多尺度功能脑网络融合特征的抑郁症分类方法,具体思想包括:首先通过精细化脑区,建立4种不同尺度的脑网络;然后对每种尺度的脑网络分别提取局部特征和全局特征,并将多种尺度脑网络的特征进行有效融合并降维;最后使用支持向量机对患者脑部功能磁共振影像进行分类.试验结果表明,分别提取局部特征和全局特征,并进行有效融合,可以提升识别效果;空间尺度减小会得到更多有效特征,进而能够有效提升分类结果;多尺度特征融合也可以在很大程度上对分类结果起到积极作用.与传统单一大尺度脑网络方法相比,本研究提出的方法获得了更加优秀的试验结果,识别率可达88.67％,充分验证了本研究提出方法的有效性和可行性,并为抑郁症患者的临床诊断与治疗提供生物学依据.

2020Chinese Journal of Medical Physics（2020）引用：5

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7Auxiliary diagnosis of Alzheimer's disease based on feature extraction

Xi LIU,Yu WANG,Changyang FU,Hongbing XIAO,Suxia XING

阿尔茨海默症(AD)是一种在老年人中多发的脑部神经疾病,致病原因迄今未明,在疾病发展早期难以诊断.随着计算机和人工智能技术的大力发展,利用磁共振成像(MRI)技术和机器学习方法辅助医生对AD进行辅助诊断不断取得新的成果.本研究提出一种基于支持向量机递归特征消除(SVM-RFE)和线性判别分析(LDA)的AD辅助诊断方法.首先对MRI图像进行预处理,获得90个大脑脑区的灰质体积;然后使用SVM-RFE和LDA相结合的方法,对90个大脑脑区灰质体积进行特征选择;最后通过SVM进行分类.通过对来自于ADNI数据库中的34名AD、26名主观记忆衰退(SMC)患者和50名正常被试(NC)的MRI图像分析,得到AD/NC、AD/SMC和NC/SMC的平均分类准确率分别为94.0％、100.0％和93.6％.实验结果证明,本研究提出的方法可有效提取样本特征,辅助医生诊断AD.

2020Chinese Journal of Medical Physics（2020）引用：1

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8Aided Diagnosis of Alzheimer's Disease Based on Structural Magnetic Resonance Imaging.

Xi Liu,Yu Wang,Changyang Fu,Hongbing Xiao,Suxia Xing

With the vigorous development of computer and pattern recognition technologies, using magnetic resonance imaging (MRI) and machine learning methods to assist diagnosis of Alzheimer's disease (AD) has became a research hotspot. A new method based on Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Linear Discriminant Analysis (LDA) for aided diagnosis of AD is proposed in this study. Firstly, Structural MRI images including 34 patients with AD, 26 subjects with subjective memory complaints (SMC) and 50 normal controls (NC) from the ADNI database are preprocessed to obtain the gray matter volumes of 90 brain regions. Then the fusion idea on SVM-RFE and LDA is used to select the characteristics on the above gray matter volumes. Finally, the selected features are classified by support vector machine (SVM), and the average classification accuracies of AD/NC, AD/SMC and NC/SMC reach respectively 94%, 100% and 93.6%. Compared with SVM-RFE or LDA alone, the average classification results on the fusion idea have obvious advantages. The above experimental results show that the proposed method can effectively extract features and assist doctors for exact diagnosis of AD and SMC.

2019ICSAI（2019）

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