Multimodal neuroimage data fusion based on multikernel learning in personalized medicine - Ran, Xue; Shi, Junyi; Chen, Yalan; Jiang, Kui (Frontiers in Pharmacology, 2022)
Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, only single neuroimaging technique, e.g., MRI (Magnetic Resonance Imaging) may omit some significant patterns which may have high relevance with the clinic target. Therefore, so far, combining different types of neuroimaging techniques, that are multi-modal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multi-kernel version for multi-modal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model, and also has its own superiority. It can explore the complementary patterns across different modal data and pay more attention to the modal data which have more significant patterns. In the experimental study, the proposed method is evaluated on the scenario of Alzheimer's disease diagnosis. The promising performance indicates the advantages of our method.