基于多图核的迁移学习方法

医学数据标注成本高昂,不同研究中心提供的脑影像数据间存在分布差异,无法有效整合,影响预测模型性能.针对此问题,文中提出基于多图核的迁移学习方法,将不同的图核用于挖掘脑网络结构信息并衡量脑网络间的相似性.提出多核学习框架,提高迁移模型的性能.在自闭症谱系障碍(ASD)多中心数据集上验证文中方法可有效利用脑网络数据的结构信息.多核学习框架也可综合不同图核的优点,进一步提高方法在脑网络数据上的分类性能.

Multi-graph Kernel Based Transfer Learning Method

Labeling medical data is costly and there are differences in the distributions of the neuroimaging data provided by different research centers. Therefore, it is nearly impossible to improve the diagnosis results by integrating the data. A multi graph-kernel based transfer learning method is proposed to tackle with this problem. Several different graph kernels are employed to mine structure information from brain network data and measure the similarity between brain networks. Then, the performance of transfer learning model is improved by a proposed multi-kernel learning framework. Experiments on the multi-center dataset of autistic spectrum disorder(ASD) indicate the classification performance on brain network data is improved and the advantages of different graph kernels are efficiently utilized by multi-kernel learning framework.