U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

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 …

Colonoscopy is an effective technique for detecting colorectal polyps, which are highly
related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy …

Accurate segmentation of polyps from colonoscopy images plays a critical role in the
diagnosis and cure of colorectal cancer. Although effectiveness has been achieved in 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 …

Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in
natural scenes. Accurate COD suffers from a number of challenges associated with low …