Results 251 to 260 of about 6,547,057 (287)

IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation

Computers in Biology and Medicine, 2021
Accurate segmentation of medical images plays an essential role in their analysis and has a wide range of research and application values in fields of practice such as medical research, disease diagnosis, disease analysis, and auxiliary surgery. In recent years, deep convolutional neural networks have been developed that show strong performance in ...
Siyuan, Chen, Yanni, Zou, Peter X, Liu
openaire   +2 more sources

TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers

Medical Image Anal.
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into ...
Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, M. Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou   +15 more
semanticscholar   +1 more source

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

Computer-Aided Analysis of Gastrointestinal Videos, 2018
Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net architectures composed of encoders and decoders are very popular for segmentation of medical images, satellite images etc.
V. Iglovikov, Alexey A. Shvets
semanticscholar   +1 more source

Information Flow Through U-Nets

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Deep Neural Networks (DNNs) have become ubiquitous in medical image processing and analysis. Among them, U-Nets are very popular in various image segmentation tasks. Yet, little is known about how information flows through these networks and whether they are indeed properly designed for the tasks they are being proposed for.
Suemin Lee, Ivan V. Bajic
openaire   +1 more source

MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation

2021
It is a challenging task to segment brain tumors from multi-modality MRI scans. How to segment and reconstruct brain tumors more accurately and faster remains an open question. The key is to effectively model spatial-temporal information that resides in the input volumetric data.
Changchen Zhao, Zhiming Zhao, Qingrun Zeng, Yuanjing Feng   +3 more
openaire   +1 more source

A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation

IET Image Processing
Medical images often exhibit low and blurred contrast between lesions and surrounding tissues, with considerable variation in lesion edges and shapes even within the same disease, leading to significant challenges in segmentation.
Jiangtao Wang, N. Ruhaiyem, Panpan Fu
semanticscholar   +1 more source

Computer vision-based concrete crack detection using U-net fully convolutional networks

Automation in Construction, 2019
For the first time, U-Net is adopted to detect the concrete cracks in the present study. Focal loss function is selected as the evaluation function, and the Adam algorithm is applied for optimization.
Zhenqing Liu, Yiwen Cao, Yize Wang, Wei Wang   +3 more
semanticscholar   +1 more source

I2U-Net: A dual-path U-Net with rich information interaction for medical image segmentation

Medical Image Anal.
Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers.
Duwei Dai, C. Dong, Qingsen Yan, Yongheng Sun, Chunyan Zhang, Zongfang Li, Song-Yuan Xu   +6 more
semanticscholar   +1 more source

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Annual Conference on Medical Image Understanding and Analysis, 2017
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing
Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo   +4 more
semanticscholar   +1 more source

UCM-Net: A U-Net-Like Tampered-Region-Related Framework for Copy-Move Forgery Detection

IEEE transactions on multimedia
Copy-move forgery causes a big challenge to copy-move forgery detection (CMFD) due to that the photometrical characteristics of genuine and tampered regions in the same image remain highly consistent.
S. Weng, Tangguo Zhu, Tiancong Zhang, Chunyu Zhang   +3 more
semanticscholar   +1 more source