SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts
This addresses segmentation challenges for low-contrast medical images, offering a training-free solution that is incremental over existing LVM-based approaches.
The paper tackled the problem of poor performance in training-free few-shot medical image segmentation by improving negative prompt quality, achieving results comparable to state-of-the-art training-based methods.
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.