Noise-Induced Cross-Modal Information Interaction and Dual-Prompt Learning for Medical Image Segmentation
Accurate medical image segmentation plays a vital role in clinical diagnostics by facilitating the precise delineation of anatomical structures and pathological regions. However, the performance of existing segmentation methods is often constrained by the scarcity of high-quality annotated datasets, as manual labeling is both labor-intensive and reliant on domain-specific expertise. To address this limitation without requiring additional annotations, we propose a novel multimodal segmentation framework that leverages medical text annotations as an auxiliary modality to complement visual information. In particular, our approach introduces a learnable encoding strategy for joint distribution modeling of image and text, which enables discriminative fusion and effectively suppresses cross-modal redundancy. Moreover, we innovatively design a frequency-domain prompt encoder based on the discrete wavelet transform (DWT) to capture multi-frequency features, thereby significantly enhancing the model’s ability to delineate fine-grained boundaries. Overall, our framework integrates cross-attention for effective cross-modal interaction, employs joint distribution modeling to enable discriminative and redundancy-reduced multimodal fusion, and incorporates auxiliary supervision to strengthen the learning of task-relevant features. Extensive experiments on nine public datasets across three clinical tasks—including cell, lung infection, and polyp segmentation—demonstrate that our method achieves competitive segmentation performance while maintaining favorable computational efficiency. Comprehensive ablation studies and feature distribution visualizations further validate the effectiveness and robustness of our proposed components. The code will be made publicly available at https://github.com/chenpeng052/MDFP