Dual-Domain Visual Prompt Learning for Multi-Modal Medical Image Saliency Prediction

Medical image saliency prediction plays a pivotal role in emulating clinician visual attention to prioritize diagnostically critical regions. Current methods remain constrained by their spatial-domain dependency and limited cross-modality generalizability, neglecting frequency-domain patterns critical for subtle pathology detection while suffering from over-specialization in specific imaging modalities. Therefore, we propose a dual-domain visual prompt network (DVPNet) that integrates cross-modality generalization with spectral pattern awareness. On the one hand, DVPNet establishes a dataset prompt branch that dynamically modulates spatial feature encoding through modality-specific priors, allowing adaptive interpretation of heterogeneous medical imaging domains. On the other hand, a spatial-frequency hybrid prompt module employs learnable wavelet filters to decompose images into multi-scale spectral components, preserving low-frequency anatomical context while enhancing discriminative high-frequency biomarkers that are typically obscured in previous pixel-level analysis. By seamlessly integrating these complementary representations, DVPNet optimally synthesizes spatial and spectral evidence, enabling robust generalization across diverse medical imaging modalities while sustaining computational efficiency. Extensive experimental results on two distinct datasets demonstrate that the proposed method outperforms state-of-the-art approaches, showing superior saliency prediction performance and enhanced generalizability across medical contexts.