Self-Prompting Large Vision Models for Few-Shot Medical Image
Segmentation
- URL: http://arxiv.org/abs/2308.07624v1
- Date: Tue, 15 Aug 2023 08:20:07 GMT
- Title: Self-Prompting Large Vision Models for Few-Shot Medical Image
Segmentation
- Authors: Qi Wu, Yuyao Zhang, Marawan Elbatel
- Abstract summary: We propose a novel perspective on self-prompting in medical vision applications.
We harness the embedding space of the Segment Anything Model to prompt itself through a simple yet effective linear pixel-wise classifier.
We achieve competitive results on multiple datasets.
- Score: 14.135249795318591
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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).
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