The application of deep learning in the medical field has continuously made huge
breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net …

Medical image segmentation is a crucial and intricate process in medical image processing
and analysis. With the advancements in artificial intelligence, deep learning techniques …

Deep learning architectures based on convolutional neural network (CNN) and Transformer
have achieved great success in medical image segmentation. Models based on the encoder …

Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …

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 …

Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

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 …

Recently, deep learning has become much more popular in computer vision applications.
The Convolutional Neural Network (CNN) has brought a breakthrough in image …

For the past several years, semantic segmentation method based on deep learning,
especially Unet, have achieved tremendous success in medical image processing. The U …

In the past few years, convolutional neural networks (CNNs) have achieved milestones in
medical image analysis. In particular, deep neural networks based on U-shaped architecture …