Transformers have shown great promise in medical image segmentation due to their ability
to capture long-range dependencies through self-attention. However, they lack the ability to …

Colonoscopy is widely recognised as the gold standard procedure for the early detection of
colorectal cancer (CRC). Segmentation is valuable for two significant clinical applications …

This paper presents a novel supervised convolutional neural network architecture,“DUCK-
Net”, capable of effectively learning and generalizing from small amounts of medical images …

Background Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐
and inter‐observer variability. An automated deep learning approach to fast and accurate …

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 …

Transformers have shown great success in medical image segmentation. However,
transformers may exhibit a limited generalization ability due to the underlying single-scale …

Recently, deep convolutional neural networks (CNNs) have provided us an effective tool for
automated polyp segmentation in colonoscopy images. However, most CNN-based …

Colorectal polyp segmentation can help physicians screen colonoscopy images, which is
essential for preventing colorectal cancer. The segmentation of polyps encounters multiple …

In this paper, we are the first to propose a new graph convolution-based decoder namely,
Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image …

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