Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …

Recently, deep learning has become much more popular in computer vision applications.
The Convolutional Neural Network (CNN) has brought a breakthrough in image …

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

Recent years have witnessed a growing interest in the use of U-Net and its improvement. It
is one of the classic semantic segmentation networks with an encoder-decoder architecture …

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

The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

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 …

In recent years, deep learning-based networks have achieved state-of-the-art performance
in medical image segmentation. Among the existing networks, U-Net has been successfully …