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 and accurate polyp segmentation plays an essential role in early colorectal
cancer diagnosis. However, it has always been a challenging task due to 1) the diverse …

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 …

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

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 …

Background and Aims Publicly available databases containing colonoscopic imaging data
are valuable resources for artificial intelligence (AI) research. Currently, little is known …

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 …

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 …

Medical image segmentation is an essential process to assist clinics with computer-aided
diagnosis and treatment. Recently, a large amount of convolutional neural network (CNN) …

Purpose Segmenting medical images accurately and reliably is important for disease
diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes …