The recently introduced Segment Anything Model (SAM) combines a clever architecture and
large quantities of training data to obtain remarkable image segmentation capabilities …

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 …

The Segment Anything Model (SAM) is a new image segmentation tool trained with the
largest available segmentation dataset. The model has demonstrated that, with prompts, it …

The Segment Anything Model (SAM) represents a state-of-the-art research advancement in
natural image segmentation, achieving impressive results with input prompts such as points …

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 …

Deep learning based automatic medical image segmentation models often suffer from
domain shift, where the models trained on a source domain do not generalize well to other …

The Segment Anything Model (SAM) has achieved a notable success in two-dimensional
image segmentation in natural images. However, the substantial gap between medical and …

Due to the flexibility of prompting, foundation models have become the dominant force in the
domains of natural language processing and image generation. With the recent introduction …

In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced segmentation model
that utilizes the SAM 2 framework to address both 2D and 3D medical image segmentation …

Due to the inherent flexibility of prompting, foundation models have emerged as the
predominant force in the fields of natural language processing and computer vision. The …