Results 31 to 40 of about 6,547,057 (287)
Local brain-age: A U-Net model [PDF]
Frontiers in Aging Neuroscience, 2021 AbstractWe propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing.
Sebastian G. Popescu +6 more
openaire +7 more sources
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation [PDF]
Bildverarbeitung für die Medizin, 2018 The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation.
Fabian Isensee +10 more
semanticscholar +1 more source
Mixed Transformer U-Net for Medical Image Segmentation [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021 Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their ...
Hongyi Wang +6 more
semanticscholar +1 more source
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
, 2022 Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies.
Jia, Xi +5 more
openaire +2 more sources
FreeU: Free Lunch in Diffusion U-Net [PDF]
Computer Vision and Pattern Recognition, 2023 In this paper, we uncover the untapped potential of dif-fusion U-Net, which serves as a “free lunch” that substan-tially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising
Chenyang Si +3 more
semanticscholar +1 more source
DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation [PDF]
Frontiers in Bioengineering and Biotechnology, 2023 Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with
Guanqun Sun +7 more
semanticscholar +1 more source
Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network
Frontiers in Bioengineering and Biotechnology, 2022 Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model.Methods: In this study, a fully
Yang Li +8 more
doaj +1 more source
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation [PDF]
Comput. Biol. Medicine, 2022 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 image segmentation and has been widely applied in a wide range of practical
Qing Xu, Wenting Duan, Nana He
semanticscholar +1 more source
Chaining a U-Net With a Residual U-Net for Retinal Blood Vessels Segmentation
IEEE Access, 2020 Retina images are the only non-invasive way of accessing the cardiovascular system, offering us a means of observing patterns such as microaneurysms, hemorrhages and the vasculature structure which can be used to diagnose a variety of diseases.
Gendry Alfonso Francia +3 more
doaj +1 more source
AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images [PDF]
IEEE Transactions on Medical Imaging, 2022 Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially
Gongping Chen +3 more
semanticscholar +1 more source