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January 1, 2025IET Computer VisionOpen Access

Self‐Prompting Segment Anything Model for Few‐Shot Medical Image Segmentation

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Authors

HZHaifeng ZhaoWLWeichen LiuLMLeilei Ma

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Overview

Integration of prior knowledge improves few-shot medical image segmentation, highlighting SAM's potential benefits.

Key Points

  • The proposed method enhances few-shot medical image segmentation, achieving better results than existing approaches.
  • Using the Isolated Noise Removal (INR) module significantly improves mask accuracy and reduces noise.
  • The Multi-point Automatic Prompt (MPAP) module effectively generates point prompts, optimizing the segmentation process.
  • Our approach demonstrates superior performance on benchmark datasets, advancing the field of medical image segmentation.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68af59ddad7bf08b1eade9c3https://doi.org/10.1049/cvi2.70040
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multiple Point MedSAM Prompting for Enhanced Medical Image Segmentation2026
  2. 2MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation2024 · 4 citations
  3. 3SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting2025
  4. 4SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction2024
  5. 5SAM Fewshot Finetuning for Anatomical Segmentation in Medical Images2024