Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

Recently, ConvNeXts constructing from standard ConvNet modules has produced
competitive performance in various image applications. In this paper, an efficient model …

Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …

Accurate and automatic segmentation of medical images can greatly assist the clinical
diagnosis and analysis. However, it remains a challenging task due to (1) the diversity of …

Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical
image analysis with a vast number of new methods being proposed each year. This ever …

Recently, a growing interest has been seen in deep learning-based semantic segmentation.
UNet, which is one of deep learning networks with an encoder-decoder architecture, is …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

In this paper, we embed two types of attention modules in the dilated fully convolutional
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …

Medical image segmentation is one of the important steps in medical image analysis and
has a wide range of applications and research values in medical research and practice …