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

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 …

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

For the task of medical image segmentation, fully convolutional network (FCN) based
architectures have been extensively used with various modifications. A rising trend in these …

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

As an important building block in automatic medical systems, image segmentation has made
great progress due to the data-driving mechanism of deep architecture. Recently, numerous …

Segmentation is a fundamental task in medical image analysis. However, most existing
methods focus on primary region extraction and ignore edge information, which is useful for …

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 …

Accurate and automated segmentation of medical images is crucial for enhancing the
efficiency of disease diagnosis and treatment. In the past few years, there has been a …