Medical image segmentation is a fundamental task in the community of medical image
analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer …

Recently, deep learning methods have achieved state-of-the-art performance in many
medical image segmentation tasks. Many of these are based on convolutional neural …

Over the past few years, convolution neural networks (CNNs) and vision transformers (ViTs)
have been two dominant architectures in medical image segmentation. Although CNNs can …

Medical image segmentation is crucial for the development of computer-aided diagnostic
and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly …

Convolutional neural networks (CNNs) have proved to be powerful medical image
segmentation models. In this study, we address some of the main unresolved issues …

Convolutional neural networks (CNNs) have strong ability to extract local features, but it is
slightly lacking in extracting global contexts. In contrast, transformers are good at long …

Current research on deep learning for medical image segmentation exposes their limitations
in learning either global semantic information or local contextual information. To tackle these …

Pre‐segmentation is known as a crucial step in medical image analysis. Many approaches
have been proposed to make improvement to both the quality and efficiency of …

Deep learning has shown great potential for automated medical image segmentation to
improve the precision and speed of disease diagnostics. However, the task presents …

Automated medical image segmentation is a crucial step in clinical analysis and diagnosis,
as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks …