核磁共振成像技术（Magnetic Resonance Imaging，MRI）作为一种记录脑结构或功能的无创技术，为研究精神疾病的神经基础提供了有力工具。受制于精神疾病的复杂性和难以明确的发病机制，目前临床诊断主要依据患者主诉症状和定性诊断，总体疗效欠佳，急需客观影像学标记辅助诊疗。本博士课题主要研究面向精神疾病分类诊断和磁共振影像的机器学习算法，在传统算法的特征提取、特征融合，以及流行的深度学习算法的可解释性和多尺度图卷积网络方面提出了新的改进或突破，并将其应用于多种脑疾病与健康对照的区别诊断和交叉验证，提高了基于 MRI数据的分类性能。本文的主要创新工作包括：
Magnetic Resonance Imaging (MRI), as a noninvasive technique for depicting brain structure or function, provides a useful tool for the study of the neural basis of mental disorders. Due to the complexity of mental disorders, the pathogenesis of various diseases is not clear. At present, diagnosis is mainly based on the symptoms complained by patients and qualitative diagnosis, with poor overall efficacy, and objective imaging markers are urgently needed to assist diagnosis and treatment. This doctoral project mainly studies machine learning algorithms for mental disease classification diagnosis and magnetic resonance imaging. Several new improvements or breakthroughs have been proposed in the aspects of feature extraction and feature fusion of traditional algorithms, as well as the interpretation of popular deep learning algorithms and multi-scale map convolutional network. The proposed algorithm has been applied to the differential diagnosis and cross-validation of various brain diseases and healthy controls to improve the classification performance based on MRI data. The main innovative work of this paper includes:
In the first work, we propose an ensemble learning system for a 4-way classification of Alzheimer's Disease and Mild Cognitive Impairment. The model made hierarchical classification mainly based on the relative importance of features. Four categories of subjects, including two subtypes of MCI (EMCI, LMCI), AD patients, and age-matched healthy controls, were divided into multiple two-category tasks to obtain the final category for each subject. The algorithm was applied to four categories of subjects in ADNI data, 100 cases in each type, and cortical information was generated based on Freesurfer software. The experimental results show that the feature selection algorithm based on relative importance can better promote the classification performance than the standard dimension-reduction methods based on minimizing the feature space. Besides, the hippocampus, precuneus, temporal lobe, etc., as well as scale and gender, are useful features retained after feature selection, which have been confirmed in many previous studies to describe the differences between AD, MCI, and healthy controls to a certain extent.
|Keyword||脑影像 连接网路 特征选择 图卷积|
|Sub direction classification||医学影像处理与分析|
|姚东任. 面向精神疾病分类诊断的磁共振影像机器学习算法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2021.|
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