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 …

Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …

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 …

Abstract U-Net 网络在医学图像分割任务中取得了很好的成绩. 近年来, 众多学者针对 U-Net
结构不断地进行研究和扩展, 比如编, 解码器的改进和跳跃连接的改进. 本文针对基于 U-Net …

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 …

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 analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

The U-Net has become the most popular structure in medical image segmentation in recent
years. Although its performance for medical image segmentation is outstanding, a large …

This paper provides a comprehensive review of U-Net networks. It describes the basic
structure and working principle of convolutional neural networks and U-Net networks …

Recently, with the increasing application of deep learning in the medical field, convolutional
neural networks, represented by U-Net, has been widely applied in medical image …