Results 21 to 30 of about 295,660 (323)
U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism
IEEE Access, 2023 The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be time-consuming and delay diagnosis.
Kittipol Wisaeng
doaj +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U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?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
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U-Net-Based Medical Image Segmentation
Journal of Healthcare Engineering, 2022 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 scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than ...
Xiao-Xia Yin +4 more
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Deep segmentation networks predict survival of non-small cell lung cancer [PDF]
, 2019 Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed
Allen, Bryan +16 more
core +2 more sources
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
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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
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Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation
IEEE Access, 2021 The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain.
Neil Micallef +2 more
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, 2022 In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equivariant networks are suited to computer vision tasks where the same classes of objects appear at different scales, like in most semantic segmentation tasks.
Sangalli, Mateus +3 more
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Joint singing voice separation and F0 estimation with deep U-net architectures [PDF]
, 2019 Vocal source separation and fundamental frequency estimation in music are tightly related tasks. The outputs of vocal source separation systems have previously been used as inputs to vocal fundamental frequency estimation systems; conversely, vocal ...
andreas +17 more
core +1 more source
International Journal of Digital Earth, 2023 Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing ...
Hesheng Chen +7 more
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