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认知障碍脑功能磁共振图像的孪生网络特征工程算法

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功能磁共振成像技术(fMRI:functional Magnetic Resonance Imaging)是一种高效的脑成像技术研究方法，为减少fMRI数据的冗余，将其转换为更具分类潜力的特征，提出一个基于孪生网络(SANet:Siamese Network)的特征构造算法SANet，将多个扫描点下的脑区信息类比为图，应用改进的AlexNet网络进行特征构造，并结合增量特征选择策略达到优化分类的目的。通过实验对比3种不同网络结构和4种分类器对SANet模型的影响，并进行消融实验，验证增量特征选择算法对SANet构造特征的分类效果。实验表明，SANet模型能对fMRI数据进行有效构造，且提高原始特征的分类性能。
Siamese Network Based Feature Engineering Algorithm for Encephalopathy fMRI Images
fMRI(functional Magnetic Resonance imaging)is an efficient research method for brain imaging technique.In order to reduce the redundancy of the fMRI data and transform the fMRI data to the constructed features with more classification potential,a feature construction method based on the siamese network named as SANet(Siamese Network)is proposed.It engineered the brain regions features under multiple scanning points of an fMRI image.The improved AlexNet is used for feature engineering,and the incremental feature selection strategy is used to find the best feature subset for the encephalopathy prediction task.The effects of three different network structures and four classifiers on the SANet model are evaluated for their prediction efficiencies,and the ablation experiment is conducted to verify the classification effect of the incremental feature selection algorithm on the SANet features.The experimental data shows that the SANet model can construct features from the fMRI data effectively,and improve the classification performance of original features.

functional magnetic resonance imaging(fMRI)feature engineeringsiamese network(SANet)modelsiamese networkincremental feature selection

周丰丰、王倩、董广宇

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吉林大学计算机科学与技术学院,长春 130012

功能磁共振成像 特征构造 SANet模型 孪生网络 增量特征选择

国家自然科学基金资助项目国家自然科学基金资助项目吉林省中青年科技创新创业卓越人才(团队)基金(创新类)资助项目吉林省大数据智能计算实验室基金资助项目

62072212U19A206120210509055RQ20180622002JC

2024

吉林大学学报(信息科学版)
吉林大学

吉林大学学报(信息科学版)

CSTPCD
影响因子：0.607
ISSN：1671-5896
年,卷(期)：2024.42(1)
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