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 …

Recently, deep learning, especially convolutional neural networks, has achieved the
remarkable results in natural image classification and segmentation. At the same time, in the …

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 polyp segmentation is of great importance for colorectal cancer diagnosis.
However, even with a powerful deep neural network, there still exists three big challenges …

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

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 …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

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 …