SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation

arXiv:2606.17972v1 Announce Type: cross Abstract: 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 introduc

Source: arXiv cs.AI — read the full report at the original publisher.

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