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

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 …

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

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 …

Residual network (ResNet) and densely connected network (DenseNet) have significantly
improved the training efficiency and performance of deep convolutional neural networks …

Medical image segmentation is a crucial task within the realm of medical image processing.
Nevertheless, the intrinsic characteristics of medical images and the limited availability of …

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 …

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 …

Medical image segmentation is a fundamental and challenge task in many computer-aided
diagnosis and surgery systems, and attracts numerous research attention in computer vision …