The recent developments of foundation models in computer vision, especially the Segment
Anything Model (SAM), allow scalable and domain-agnostic image segmentation to serve as …

Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical
image analysis with a vast number of new methods being proposed each year. This ever …

In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical
image segmentation. It aims to augment the infusion of semantic information into low-level …

With the development of Deep Neural Networks (DNNs), many efforts have been made to
handle medical image segmentation. Traditional methods such as nnUNet train specific …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …

Abstract While the Segment Anything Model (SAM) excels in semantic segmentation for
general-purpose images, its performance significantly deteriorates when applied to medical …

Detection of colon polyps has become a trending topic in the intersecting fields of machine
learning and gastrointestinal endoscopy. The focus has mainly been on per-frame …

U-shaped network structures with skip connections played an irreplaceable role in medical
image analysis, but the limitation of convolution makes it unable to learn long-distance …

While the Segment Anything Model (SAM) excels in semantic segmentation for general-
purpose images, its performance significantly deteriorates when applied to medical images …