Research on Image Segmentation Models Based on Deep Learning and Multimodal Data Fusion

In practical applications, data insufficiency and incomplete modalities are common challenges that significantly affect the performance of image segmentation models. This paper proposes a deep learning-based multimodal data fusion segmentation model, aiming to enhance the accuracy and robustness of segmentation. To address the challenges posed by small sample sizes and incomplete multimodal data, the model introduces an online data augmentation strategy search mechanism, which alleviates the issue of insufficient labeled data through an optimal augmentation strategy. Additionally, a feature-map-based knowledge distillation strategy is employed to effectively reduce the risk of model overfitting. To represent incomplete multimodal data, a variational autoencoder is used to learn the mean and variance information of the modalities, integrating incomplete modality features based on distribution characteristics. Subsequently, a clustering layer transforms the fused features from a reconstruction-friendly feature space to a clustering-friendly feature space. For multimodal feature fusion, the model adopts a joint strategy of hyper-edge construction and hypergraph learning, which preserves higher-order semantic information and optimizes the multimodal information fusion process. Experimental results show that the proposed segmentation model outperforms the traditional UNet model in various image segmentation tasks, particularly demonstrating significant advantages in handling incomplete multimodal data. This study provides an effective solution for multimodal image segmentation under small sample conditions and offers novel approaches and insights for future research and applications in related fields.