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 …

Medical image segmentation is an important and complex task in clinical practices, but the
widely used U-Net usually cannot achieve satisfactory performances in some clinical …

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

As medical image segmentation has been one of the most widely implemented tasks in
deep learning, there have been various solutions proposed for its applications to achieve …

Medical image semantic segmentation plays a crucial role in the localization of organs and
lesions, analysis and quantification of pathologies, and surgical planning and navigation …

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 …

Medical image segmentation using deep neural networks has been highly successful.
However, the effectiveness of these networks is often limited by inadequate dense prediction …

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

A variety of convolutional neural network (CNN) based methods for medical image
segmentation have achieved outstanding performance, however, inherently suffered from a …

Medical image segmentation is an important task in modern analysis of medical images.
Current methods tend to extract either local features with convolutions or global features with …