SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912441478152192 |
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| author | Liu, Yufei Xiao, Haoke Chai, Jiaxing Zhang, Yongcun Wang, Rong Meng, Zijie Luo, Zhiming |
| author_facet | Liu, Yufei Xiao, Haoke Chai, Jiaxing Zhang, Yongcun Wang, Rong Meng, Zijie Luo, Zhiming |
| contents | The advent of Large Vision Models (LVMs) offers new opportunities for few-shot medical image segmentation. However, existing training-free methods based on LVMs fail to effectively utilize negative prompts, leading to poor performance on low-contrast medical images. To address this issue, we propose SynPo, a training-free few-shot method based on LVMs (e.g., SAM), with the core insight: improving the quality of negative prompts. To select point prompts in a more reliable confidence map, we design a novel Confidence Map Synergy Module by combining the strengths of DINOv2 and SAM. Based on the confidence map, we select the top-k pixels as the positive points set and choose the negative points set using a Gaussian distribution, followed by independent K-means clustering for both sets. Then, these selected points are leveraged as high-quality prompts for SAM to get the segmentation results. Extensive experiments demonstrate that SynPo achieves performance comparable to state-of-the-art training-based few-shot methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts Liu, Yufei Xiao, Haoke Chai, Jiaxing Zhang, Yongcun Wang, Rong Meng, Zijie Luo, Zhiming Computer Vision and Pattern Recognition The advent of Large Vision Models (LVMs) offers new opportunities for few-shot medical image segmentation. However, existing training-free methods based on LVMs fail to effectively utilize negative prompts, leading to poor performance on low-contrast medical images. To address this issue, we propose SynPo, a training-free few-shot method based on LVMs (e.g., SAM), with the core insight: improving the quality of negative prompts. To select point prompts in a more reliable confidence map, we design a novel Confidence Map Synergy Module by combining the strengths of DINOv2 and SAM. Based on the confidence map, we select the top-k pixels as the positive points set and choose the negative points set using a Gaussian distribution, followed by independent K-means clustering for both sets. Then, these selected points are leveraged as high-quality prompts for SAM to get the segmentation results. Extensive experiments demonstrate that SynPo achieves performance comparable to state-of-the-art training-based few-shot methods. |
| title | SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.15153 |