医学图像分割不用再堆复杂解码器了，直接看SegDINO怎么用DINO特征加轻量多尺度建模做到又快又准。
SegDINO提出一种高效分割框架，基于DINOv3骨干网络，通过Token Pyramid Adaptation（TPA）将中间DINO特征重组为伪多尺度层次，并引入Scale-Aware Decoding（SAD）进行高效尺度内优化和自顶向下多尺度传播。作者还构建了PanCT数据集，包含284例患者专家标注的胰腺肿瘤CT图像，用于评估对小病灶的处理能力。在PanCT和三个公开基准上，SegDINO以更高效率达到SOTA结果。代码已开源。
SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation
Self-supervised DINO models provide strong transferable visual representations, yet applying them directly to image segmentation remains challenging. Existing approaches commonly rely on heavy decoders with complex upsampling, introducing substantial parameter and computational overhead. We observe that introducing scale into DINO features is far more critical than increasing decoder capacity. In this work, we present SegDINO, an efficient segmentation framework that integrates a DINOv3 backbone with lightweight scale modeling. SegDINO introduces Token Pyramid Adaptation (TPA) to reorganize intermediate DINO features into a pseudo multi-scale hierarchy, and Scale-Aware Decoding (SAD) for efficient intra-scale refinement and top-down multi-scale propagation. We further curate PanCT, a new CT dataset containing 284 patients with expert-annotated pancreatic tumors, to assess SegDINO's ability to handle difficult small-lesion cases. Extensive experiments on PanCT and three public benchmarks demonstrate that SegDINO achieves state-of-the-art results with high efficiency. The code is available at https://github.com/script-Yang/segdino_v2.