  We propose SAMed, a general solution for medical image segmentation.
Different from the previous methods, SAMed is built upon the large-scale image
segmentation model, Segment Anything Model (SAM), to explore the new research
paradigm of customizing large-scale models for medical image segmentation.
SAMed applies the low-rank-based (LoRA) finetuning strategy to the SAM image
encoder and finetunes it together with the prompt encoder and the mask decoder
on labeled medical image segmentation datasets. We also observe the warmup
finetuning strategy and the AdamW optimizer lead SAMed to successful
convergence and lower loss. Different from SAM, SAMed could perform semantic
segmentation on medical images. Our trained SAMed model achieves 81.88 DSC and
20.64 HD on the Synapse multi-organ segmentation dataset, which is on par with
the state-of-the-art methods. We conduct extensive experiments to validate the
effectiveness of our design. Since SAMed only updates a small fraction of the
SAM parameters, its deployment cost and storage cost are quite marginal in
practical usage. The code of SAMed is available at
https://github.com/hitachinsk/SAMed.
