Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent studies have demonstrated that biomarkers from multiple modalities contain complementary information for the diagnosis of Alzheimer's disease AD and its prodromal stage mild cognitive impairment MCI. In order to fuse data from multiple modalities, most previous approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. Hover, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the ights assigned to each modality. ,  propose a nonlinear graph fusion method to efficiently exploit the complementarity in the multi-modal data for the classification of AD. Specifically, a graph is first constructed for each modality individually. Afterwards, a single unified graph is obtained via a nonlinear combination of the graphs in an iterative cross diffusion process. Using the unified graphs,  achieved classification accuracies of 91.8% beten AD subjects and normal controls NC, 79.5% beten MCI subjects and NC and 60.2% in a three-way classification, which are competitive with state-of-the-art results.
['Tong Tong', 'Katherine R. Gray', 'Qinquan Gao', 'Liang Chen', 'Daniel Rueckert']
I am writing a research paper on a idea Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer's Disease
[{"from": "human", "value": "I am writing a research paper on a idea Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer's Disease"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent studies have demonstrated that biomarkers from multiple modalities contain complementary information for the diagnosis of Alzheimer's disease AD and its prodromal stage mild cognitive impairment MCI. In order to fuse data from multiple modalities, most previous approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. Hover, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the ights assigned to each modality. ,  propose a nonlinear graph fusion method to efficiently exploit the complementarity in the multi-modal data for the classification of AD. Specifically, a graph is first constructed for each modality individually. Afterwards, a single unified graph is obtained via a nonlinear combination of the graphs in an iterative cross diffusion process. Using the unified graphs,  achieved classification accuracies of 91.8% beten AD subjects and normal controls NC, 79.5% beten MCI subjects and NC and 60.2% in a three-way classification, which are competitive with state-of-the-art results."}]