The precise segmentation of medical images is one of the key challenges in pathology
research and clinical practice. However, many medical image segmentation tasks have …

Accurate medical image segmentation is of great significance for subsequent diagnosis and
analysis. The acquisition of multi-scale information plays an important role in segmenting …

Medical image segmentation result is an essential reference for disease diagnosis.
Recently, with the development and application of convolutional neural networks, medical …

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) …

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 …

In recent years, precise medical image segmentation methods based on the encoder–
decoder structure have attracted much attention, but there are still some limitations. They …

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

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …

Accurate and automatic segmentation of medical images is a crucial step for clinical
diagnosis and analysis. The convolutional neural network (CNN) approaches based on the …

Accurate segmentation of medical images is crucial for clinical diagnosis and evaluation.
However, medical images have complex shapes, the structures of different objects are very …