ACL-Net: Attribute-Aware Contrastive Learning Network for Medical Image Fusion

Medical image fusion aims to integrate multi-sensor source images into a unified representation, providing comprehensive and diagnostically enriched information to support clinical decision-making. However, the scarcity of labeled data presents significant challenges in effectively learning complementary features across modalities. In this paper, we propose a novel attribute-aware contrastive learning network, called ACL-Net, boosting medical image fusion performance. Specifically, we introduce the attribute transformation strategy to simulate variations in pixel intensity and structural patterns, guiding the model to focus on critical cross-modal information. In this way, it enhances contrastive learning by generating diverse negative pairs, thereby mitigating the scarcity of negative samples in unsupervised fusion scenarios. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art medical image fusion methods.