用轻量结构增强DINO多尺度能力，高效实现医学图像分割
SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation
- 通过特征重组构建伪多尺度结构，替代复杂解码器
- 在4个数据集上实现顶尖性能，参数量减少30%以上
- 特别适合小病灶分割，适合医疗影像研究者使用
自监督DINO模型具备强大的可迁移视觉表征，但直接用于图像分割仍具挑战。现有方法通常依赖重型解码器和复杂上采样，带来大量参数与计算开销。我们发现，在DINO特征中引入尺度信息比提升解码器容量更为关键。本文提出SegDINO，一个基于DINOv3主干的高效分割框架，引入令牌金字塔适配（TPA）将中间特征重构成伪多尺度层级，并采用尺度感知解码（SAD）实现高效的尺度内精修与自顶向下多尺度传播。此外，我们构建了新的胰腺CT数据集PanCT，包含284名患者、专家标注的胰腺肿瘤，用于评估小病灶分割能力。在PanCT及三个公开基准上的大量实验表明，SegDINO以高效率实现最先进性能。代码已开源。
原文摘要 · Abstract (English)
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.
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