Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …

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 …

Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative
analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent …

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

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 …

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by
polyps. Therefore, the segmentation of polyps has garnered significant attention in the field …

The polyp segmentation technology based on deep learning could better and faster help
doctors diagnose the polyps in the intestinal wall, which are predecessors of colorectal …

Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related
mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early …

Medical image segmentation is widely used in clinical diagnosis, and methods based on
convolutional neural networks have been able to achieve high accuracy. However, it is still …