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

Although various methods based on convolutional neural networks have improved the
performance of biomedical image segmentation to meet the precision requirements of …

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 …

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

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 …

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 …

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

ABSTRACT∗ Transformer completement convolutional neural network (CNN) has achieved
better performance than improved CNN-based methods. Especially, Transformer is utilized …

Medical image segmentation is one of the most fundamental tasks concerning medical
information analysis. Various solutions have been proposed so far, including many deep …

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