Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art
performance in recent years. Particularly, in deep learning, U-Net model has been …

Medical image segmentation is a challenging task due to the high variation in shape, size
and position of infections or lesions in medical images. It is necessary to construct multi …

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 crucial for diagnosis and treatment planning. While recent
advancements in deep learning, particularly UNet variants, have improved segmentation …

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 …

U-Net has been providing state-of-the-art performance in many medical image segmentation
problems. Many modifications have been proposed for U-Net, such as attention U-Net …

This paper proposes a novel U-Net variant using stacked dilated convolutions for medical
image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with …

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 …

Medical image segmentation is the primary measure of medical image analysis. With the
development of deep learning, U-net based approaches have presented for different …