Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
This work addresses the problem of improving autism diagnosis and biomarker discovery for medical researchers and clinicians, representing an incremental advance in graph learning methods for neuroimaging data.
The authors tackled the challenge of multi-modal integration and classification for disease prediction by proposing MMKGL, a method that constructs adaptive multi-modal graphs and uses multi-kernel learning to extract heterogeneous information, achieving state-of-the-art performance on the ABIDE dataset for autism prediction and identifying discriminative brain regions.
Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.