  The emerging scale segmentation model, Segment Anything (SAM), exhibits
impressive capabilities in zero-shot segmentation for natural images. However,
when applied to medical images, SAM suffers from noticeable performance drop.
To make SAM a real ``foundation model" for the computer vision community, it is
critical to find an efficient way to customize SAM for medical image dataset.
In this work, we propose to freeze SAM encoder and finetune a lightweight
task-specific prediction head, as most of weights in SAM are contributed by the
encoder. In addition, SAM is a promptable model, while prompt is not
necessarily available in all application cases, and precise prompts for
multiple class segmentation are also time-consuming. Therefore, we explore
three types of prompt-free prediction heads in this work, include ViT, CNN, and
linear layers. For ViT head, we remove the prompt tokens in the mask decoder of
SAM, which is named AutoSAM. AutoSAM can also generate masks for different
classes with one single inference after modification. To evaluate the
label-efficiency of our finetuning method, we compare the results of these
three prediction heads on a public medical image segmentation dataset with
limited labeled data. Experiments demonstrate that finetuning SAM significantly
improves its performance on medical image dataset, even with just one labeled
volume. Moreover, AutoSAM and CNN prediction head also has better segmentation
accuracy than training from scratch and self-supervised learning approaches
when there is a shortage of annotations.
