Y Yin, Z Han, M Jian, GG Wang, L Chen… - Computers in Biology and …, 2023 - Elsevier
In recent years, Unet and its variants have gained astounding success in the realm of medical image processing. However, some Unet variant networks enhance their …
Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image …
A Lou, S Guan, M Loew - Medical Imaging 2021: Image …, 2021 - spiedigitallibrary.org
Recently, deep learning has become much more popular in computer vision applications. The Convolutional Neural Network (CNN) has brought a breakthrough in image …
Current state-of-the-art medical image segmentation techniques predominantly employ the encoder–decoder architecture. Despite its widespread use, this U-shaped framework …
G Sun, Y Pan, W Kong, Z Xu, J Ma… - … in Bioengineering and …, 2024 - frontiersin.org
Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants …
Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …
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 …
In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation. It aims to augment the infusion of semantic information into low-level …
Q Xu, Z Ma, HE Na, W Duan - Computers in Biology and Medicine, 2023 - Elsevier
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 …