Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

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 …

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 …

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 …

The image semantic segmentation has been extensively studying. The modern methods rely
on the deep convolutional neural networks, which can be trained to address this problem. A …

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 …

In recent years, semantic segmentation method based on deep learning provides advanced
performance in medical image segmentation. As one of the typical segmentation networks …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

The context-based convolutional neural network (CNN) is one of the most well-known CNNs
to improve the performance of semantic segmentation. It has achieved remarkable success …