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 …

U-Net is a widely adopted neural network in the domain of medical image segmentation.
Despite its quick embracement by the medical imaging community, its performance suffers …

Deep neural networks are now widely used in the medical image segmentation field for their
performance superiority and no need of manual feature extraction. U-Net has been the …

Medical image segmentation is a critical task in healthcare diagnostics and treatment
planning. Among various approaches, the U-Net neural network has emerged as a …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

Deep learning has been widely utilized for medical image segmentation. The most
commonly used U-Net and its variants often share two common characteristics but lack solid …

The simple and efficient network structure of U-Net is widely used in medical image
segmentation, and many scholars have made various researches on the U-Net structure …

Medical image segmentation is crucial for disease diagnosis and monitoring. Though
effective, the current segmentation networks such as UNet struggle with capturing long …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …

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 …