Medical SAM adapter: Adapting segment anything model for medical image segmentation
National University of Singapore · University of Alberta · +4 more institutions
Abstract
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation due to the lack of medical-specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation. Med-SA uses a light yet effective adaptation technique instead of fine-tuning the SAM model, incorporating domain-specific medical…
Citation impact
- FWCI
- 306.13
- Percentile
- 100%
- References
- 90
Authors
8Topics & keywords
- Artificial intelligence
- Computer vision
- Segmentation
- Computer science
- Adapter (computing)
- Image segmentation
- Image (mathematics)