DINO Prior Calibration for Global Context Modulation in Medical Image Segmentation

Medical image segmentation requires local details and global context, but unselective global information aggregation may introduce interference in regions with weak boundaries, low contrast, or similar tissues. In medical images, foreground targets and background tissues often have similar feature distributions, whereas DINO priors are mainly learned from natural images with clearer differences between objects and backgrounds. Consequently, directly introducing these priors may cause the model to attend to background regions with similar appearances or weaken its response to the true target and its boundaries. To address these issues, we calibrate DINO priors and supplement them with input image features for medical image segmentation. We propose DPCNet, a DINO Prior Calibration Network for medical image segmentation. DINO Prior Calibration uses coarse segmentation logits to calibrate multi-scale DINO priors. DPCNet then introduces the calibrated DINO priors into GECM as an independent global branch. The calibrated priors also dynamically regulate the structural frequency and selective dependency branches, which supplement the DINO prior with complementary global information from the input image. Experiments on multiple public medical image segmentation datasets demonstrate the effectiveness of DPCNet.