SegDINO reshapes DINOv3 with lightweight scale modeling, reaching top segmentation accuracy across four datasets at 27.68M parameters and 51 FPS
The work presents SegDINO, which uses a frozen DINOv3-S encoder to collect intermediate features from layers 3, 6, 9, and 12, reorganizes same-resolution tokens into a pseudo multi-scale pyramid via Token Pyramid Adaptation (TPA), and applies Scale-Aware Decoding (SAD) for intra-scale refinement and top-down inter-scale propagation, alongside a new PanCT dataset of 284 pancreatic cancer patient CT scans; on PanCT and the public TN3K, Kvasir-SEG, and ISIC benchmarks, SegDINO outperforms U-Net, SegNet, R2U-Net, Attention U-Net, TransUNet, U-NeXt, and U-KAN in both DSC and HD95, with 27.68M total parameters and 51 FPS inference.