基于多模态多标记迁移学习的早期阿尔茨海默病诊断

针对当前基于机器学习的早期阿尔茨海默病（AD）诊断中训练样本不足的问题，提出一种基于多模态特征数据的多标记迁移学习方法，并将其应用于早期阿尔茨海默病诊断。所提方法框架主要包括两大模块：多标记迁移学习特征选择模块和多模态多标记分类回归学习器模块。首先，通过稀疏多标记学习模型对分类和回归学习任务进行有效结合；然后，将该模型扩展到来自多个学习领域的训练集，从而构建出多标记迁移学习特征选择模型；接下来，针对异质特征空间的多模态特征数据，采用多核学习技术来组合多模态特征核矩阵；最后，为了构建能同时用于分类与回归的学习模型，提出多标记分类回归学习器，从而构建出多模态多标记分类回归学习器。在国际老年痴呆症数据库（ADNI）进行实验，分类轻度认知功能障碍（MCI）最高平均精度为79.1%，预测神经心理学量表测试评分值最大平均相关系数为0.727。实验结果表明，所提多模态多标记迁移学习方法可以有效利用相关学习领域训练数据，从而提高早期老年痴呆症诊断性能。

Multimodal multi-label transfer learning for early diagnosis of Alzheimer's disease

In the field of medical imaging analysis using machine learning, training samples are not enough. In order to solve the problem, a multimodal multi-label transfer learning model was proposed and applied to early diagnosis of Alzheimer's Disease (AD). Specifically, the multimodal multi-label transfer learning model consisted of two components:multi-label transfer learning feature selection and multimodal multi-label learning machine for classification and regression together. Firstly, the multi-label transfer learning feature selection model was built, which was based on the conventional sparse multi-label learning of Lasso (Least absolute shrinkage and selection operator) model for the combination of classification and regression tasks. Secondly, the technique of transfer learning was used to extend the conventional sparse multi-label learning of Lasso model and create the multi-label transfer learning feature selection model that can be performed on training samples from different learning multi-domains. Then, according to the multimodal feature data in the heterogeneous feature space, the multi-kernel learning was used to combine multimodal feature kernel matrix. Finally, the multimodal multi-label learning machine was built, and which was consisted of multi-kernel learning for the combination of multimodal biomarkers and multi-label classification and regression model. To evaluate the effectiveness of the multimodal multi-label transfer learning model, the Alzheimer's Disease Neuroimaging Initiative (ADNI) database was employed. The experimental results on the ADNI database show that the proposed model can recognize Mild Cognitive Impairment Converters (MCI-C) patients from MCI NonConverters (MCI-NC) ones with 79.1% accuracy and predict clinical scores with 0.727 correlation coefficient, so it can significantly improve the performance of early AD diagnosis with the aid of related domain knowledge.