Recently, convolutional neural network (CNN) has achieved great success in medical image
segmentation. However, due to the limitation of convolutional receptive field, the pure …

Medical image segmentation is a critical step in many imaging applications. Automatic
segmentation has gained extensive concern using a convolutional neural network (CNN) …

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis
and analysis. Currently, the successful application of Transformers' model in the field of …

Transformer, born for long-range dependency establishment, has been widely studied as a
complementary of convolutional neural networks (CNNs) in medical image segmentation …

The hybrid architecture of convolution neural networks (CNN) and Transformer has been the
most popular method for medical image segmentation. However, the existing networks …

The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very
popular for medical image segmentation. However, it suffers from two challenges. First …

Accurate segmentation of medical images is essential for clinical decision-making, and deep
learning techniques have shown remarkable results in this area. However, existing …

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 …

Convolutional neural networks (CNN)(eg, UNet) have become the de facto standard and
attained immense success in medical image segmentation. However, CNN based methods …

Combining transformers and convolutional neural networks is considered one of the most
important directions for tackling medical image segmentation problems. To learn the long …