In the realm of medical image segmentation, both CNN-based and Transformer-based
models have been extensively explored. However, CNNs exhibit limitations in long-range …

Transformer and its variants have been widely used for medical image segmentation.
However, the large number of parameter and computational load of these models make …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Recently, some pioneering works have preferred applying more complex modules to
improve segmentation performances. However, it is not friendly for actual clinical …

Early detection and assessment of polyps play a crucial role in the prevention and treatment
of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist …

Automatic polyp segmentation can help physicians to effectively locate polyps (aka region of
interests) in clinical practice, in the way of screening colonoscopy images assisted by neural …

Medical image segmentation plays an important role in computer-aided diagnosis. Attention
mechanisms that distinguish important parts from irrelevant parts have been widely used in …

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has
demonstrated promising performance in several segmentation tasks. As we know, polyp …

Detection of colorectal polyps through colonoscopy is an essential practice in prevention of
colorectal cancers. However, the method itself is labor intensive and is subject to human …

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 …