Medical image segmentation provides a reliable basis for diagnosis analysis and disease
treatment by capturing the global and local features of the target region. To learn global …

Accurate and automated segmentation of medical images plays a crucial role in diagnostic
evaluation and treatment planning. In recent years, hybrid models have gained considerable …

Existing medical image segmentation methods achieve impressive progress but remain
challenged by high diversity in region scales or capricious boundaries. Meanwhile, they …

Transformers have been applied to medical image segmentation tasks owing to their
excellent longrange modeling capability, compensating for the failure of Convolutional …

The purpose of medical image segmentation is to identify target organs, tissues or lesion
areas at the pixel level to help doctors evaluate and prevent diseases. Therefore, the model …

Automatic and accurate medical image segmentation is a crucial step for clinical diagnosis
and treatment planning of diseases. The advanced convolutional neural network (CNN) …

Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis
and monitoring. However, due to the complexity of medical images and the limitations of …

In recent years, deep learning achieves significant advancements in medical image
segmentation. Research finds that integrating Transformers and CNNs effectively addresses …

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

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