October 2, 2025Open Access

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

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Our approach shows significant performance improvement in medical image segmentation tasks, especially overcoming domain shifts.
Key results indicate enhancements over models like nnUNet and SwinUNet, utilizing advanced memory and attention techniques.
The framework employs a Pseudo-mask Generation module alongside a unique attention mechanism for better localized feature extraction.
Evaluation involved diverse medical imaging modalities to validate the framework's capabilities across multiple datasets.

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New framework improves image segmentation in medical fields, eliminating manual prompts and addressing domain shift.

Xing et al. (2025) studied this question.

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