Contrastive pre-training of Soft-Clustering GCN for diagnosing Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder that gradually impairs cognitive abilities. Early detection, diagnosis, and treatment are crucial for slowing the progression of the disease. In the diagnosis of Alzheimer's disease, Graph Convolutional Networks (GCN) provide a powerful tool to enhance accuracy. However, the training of GCN faces challenges due to the tedious annotation process and limited data.To address this issue, we employ contrastive learning for pre-training GCN to improve classification performance under limited data conditions. Firstly, we augment graph data through singular value decomposition, preserving the brain's primary topological structure and avoiding the loss of intrinsic semantic structure during augmentation. Secondly, we design a Soft-Clustering GCN to obtain more robust representations of brain data. Lastly, our framework clusters graphs with similar feature semantics into the same group and encourages clustering consistency between different augmentations of the same graph. In negative sampling, we select graphs from different groups as negative samples to ensure semantic differences between positive and negative samples. Experimental results demonstrate that our approach outperforms state-of-the-art methods on the Alzheimer's disease dataset.