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

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

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 …

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 …

The Transformer architecture has gained widespread acceptance in image segmentation.
However, it sacrifices local feature details and necessitates extensive data for training …

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 …

In this article, we look into some essential aspects of convolutional neural networks (CNNs)
with the focus on medical image segmentation. First, we discuss the CNN architecture …

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 …

Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale convolutional …