DualBridge: What to Transmit Between Heterogeneous Foundation Models for Domain-Generalized Medical Image Segmentation

Single-source domain generalization requires medical image segmentation models to generalize well using supervision from a single source domain. Recently, vision foundation models (VFMs) have attracted growing interest for their transferable knowledge acquired through large-scale pretraining. Self-supervised VFMs such as DINOv3 provide broadly transferable representations, whereas promptable VFMs such as SAM 3 offer segmentation-specific capabilities. However, bridging these complementary strengths is nontrivial: effective integration requires deciding what information to transmit and where to inject it. Prompt-only interfaces can discard dense semantics, while unrestricted feature fusion can increase the adaptation burden under limited medical supervision. To balance these competing demands, we propose DualBridge, a scale-selective framework that connects DINOv3 and SAM 3 through two complementary information-transfer pathways. The spatial pathway converts DINOv3-derived predictions into explicit spatial and shape prompts for SAM 3. The feature pathway complements this compact interface by selectively transferring dense semantic features. Specifically, lightweight adapters project intermediate DINOv3 features into SAM 3’s high-level representations through gated residual connections that regulate their contribution. Together, these pathways combine explicit spatial guidance with selective feature transfer, without access to target-domain data during training. Experiments on cross-domain retinal fundus and polyp segmentation benchmarks demonstrate that DualBridge outperforms state-of-the-art methods under the single-source protocol. These results support selective information transfer as an effective strategy for harnessing complementary foundation models under unseen domain shifts.