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 …

Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated
polyp segmentation, a precancerous precursor, can minimize missed rates and timely …

We propose a new convolution neural network called HarDNet-MSEG for polyp
segmentation. It achieves SOTA in both accuracy and inference speed on five popular …

We present the first comprehensive video polyp segmentation (VPS) study in the deep
learning era. Over the years, developments in VPS are not moving forward with ease due to …

Existing video polyp segmentation (VPS) models typically employ convolutional neural
networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs …

Polyp segmentation plays an important role in preventing Colorectal cancer. Although Vision
Transformer has been widely introduced in medical image segmentation to compensate the …

Clinically, proper polyp localization in endoscopy images plays a vital role in the follow-up
treatment (eg, surgical planning). Deep convolutional neural networks (CNNs) provide a …

Recently, leveraging deep neural networks for automated colorectal polyp segmentation has
emerged as a hot topic due to the favored advantages in evading the limitations of visual …

Colorectal polyp segmentation (CPS), an essential problem in medical image analysis, has
garnered growing research attention. Recently, the deep learning-based model completely …

Colonoscopy is an effective method for detecting colorectal polyps and preventing colorectal
cancer. Therefore, in clinical practice, it is very important to accurately segment the location …