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 …

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 plays a crucial role in diagnostic
evaluation and treatment planning. In recent years, hybrid models have gained considerable …

Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …

In recent years, the field of medical image segmentation has witnessed the emergence of
numerous powerful models that continually set records for various medical tasks by …

Segmentation is an important task in a wide range of computer vision applications, including
medical image analysis. Recent years have seen an increase in the complexity of medical …

Medical image semantic segmentation is a crucial technique in medical imaging processing,
providing essential diagnostic support by precisely delineating different tissue structures and …

Medical image segmentation is crucial for accurate diagnosis and treatment planning.
Traditional methods struggle with complex medical images, while recent deep learning …

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 …

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