Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

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

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 …

Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …

More than 90% of colorectal cancer is gradually transformed from colorectal polyps. In
clinical practice, precise polyp segmentation provides important information in the early …

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 …

Colonoscopy is considered the gold standard for detection of colorectal cancer and its
precursors. Existing examination methods are, however, hampered by high overall miss …

Accurate polyp segmentation is of great significance for the diagnosis and treatment of
colorectal cancer. However, it has always been very challenging due to the diverse shape …

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 …