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
The continuous evolution of self-supervised learning models like DINO necessitates research into their efficient application across specialized domains such as medical imaging.
Improved efficiency and accuracy in medical image segmentation can significantly enhance diagnostic capabilities and reduce computational burdens for healthcare providers and AI developers.
SegDINO offers a pathway to more efficient and accurate medical image segmentation by leveraging multi-scale structures within DINO models, potentially lowering hardware requirements and speeding up analysis.
- · Medical AI developers
- · Healthcare providers
- · Patients (through improved diagnostics)
- · Cloud AI infrastructure providers
- · Companies relying on less efficient legacy segmentation methods
- · Hardware manufacturers specializing in high-compute medical imaging solutions wi
Faster and more cost-effective development of AI-powered medical diagnostic tools.
Increased adoption of AI in clinical settings due to lower operational costs and higher performance.
Acceleration of personalized medicine and preventative healthcare through ubiquitous advanced imaging analysis.
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Read at arXiv cs.AI