Abstract Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment
objects well blended with surrounding environments using sparsely-annotated data for …

Accurate medical image segmentation is of great significance for computer aided diagnosis.
Although methods based on convolutional neural networks (CNNs) have achieved good …

Medical video segmentation gains increasing attention in clinical practice due to the
redundant dynamic references in video frames. However, traditional convolutional neural …

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 …

Accurate polyp segmentation is of great importance for colorectal cancer diagnosis.
However, even with a powerful deep neural network, there still exists three big challenges …

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 …

Accurate medical image segmentation is critical for early medical diagnosis. Most existing
methods are based on U-shape structure and use element-wise addition or concatenation to …

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

Transformer-based models have been widely demonstrated to be successful in computer
vision tasks by modeling long-range dependencies and capturing global representations …