Recent advancements in large foundation models have shown promising potential
in the medical industry due to their flexible prompting capability. One such
model, the Segment Anything Model (SAM), a prompt-driven segmentation model,
has shown remarkable performance improvements, surpassing state-of-the-art
approaches in medical image segmentation. However, existing methods primarily
rely on tuning strategies that require extensive data or prior prompts tailored
to the specific task, making it particularly challenging when only a limited
number of data samples are available. In this paper, we propose a novel
perspective on self-prompting in medical vision applications. Specifically, we
harness the embedding space of SAM to prompt itself through a simple yet
effective linear pixel-wise classifier. By preserving the encoding capabilities
of the large model, the contextual information from its decoder, and leveraging
its interactive promptability, we achieve competitive results on multiple
datasets (i.e. improvement of more than 15% compared to fine-tuning the mask
decoder using a few images).

通过对 SAM 的嵌入空间进行自我提示，我们提出了一种在医学视觉应用中自我提示的新视角，通过一个简单而有效的线性像素分类器，保留了大模型的编码能力、其解码器的上下文信息，并利用其互动性，我们在多个数据集上取得了竞争性的结果（与使用少量图像微调掩模解码器相比，改进超过 15%）