Accelerating vision foundation model for efficient medical image segmentation

This study identifies two practical limitations when applying SAM to medical imaging: resizing low-resolution inputs to match the pre-trained scale and uniformly processing all patches, which lead to redundant computation. Therefore, we present an efficient strategy that integrates adapter-based tuning with token pausing to enhance throughput while preserving segmentation performance. Relevant codes and models of this paper will be available at https://github.com/wuwen1994/FasterSAM.