Segmentation is a fundamental problem in surgical scene analysis using
artificial intelligence. However, the inherent data scarcity in this domain
makes it challenging to adapt traditional segmentation techniques for this
task. To tackle this issue, current research employs pretrained models and
finetunes them on the given data. Even so, these require training deep networks
with millions of parameters every time new data becomes available. A recently
published foundation model, Segment-Anything (SAM), generalizes well to a large
variety of natural images, hence tackling this challenge to a reasonable
extent. However, SAM does not generalize well to the medical domain as is
without utilizing a large amount of compute resources for fine-tuning and using
task-specific prompts. Moreover, these prompts are in the form of
bounding-boxes or foreground/background points that need to be annotated
explicitly for every image, making this solution increasingly tedious with
higher data size. In this work, we propose AdaptiveSAM - an adaptive
modification of SAM that can adjust to new datasets quickly and efficiently,
while enabling text-prompted segmentation. For finetuning AdaptiveSAM, we
propose an approach called bias-tuning that requires a significantly smaller
number of trainable parameters than SAM (less than 2\%). At the same time,
AdaptiveSAM requires negligible expert intervention since it uses free-form
text as prompt and can segment the object of interest with just the label name
as prompt. Our experiments show that AdaptiveSAM outperforms current
state-of-the-art methods on various medical imaging datasets including surgery,
ultrasound and X-ray. Code is available at
this https URL

手术场景分析中使用人工智能的分割是一个基本问题。然而，这个领域内固有的数据稀缺性使得为该任务调整传统分割技术变得具有挑战性。因此，我们提出了一种名为 AdaptiveSAM 的改进模型，可以快速有效地适应新的数据集，并可以使用文本提示进行分割，其在各种医学图像数据集上优于当前最先进的方法。