Road Extraction by Deep Residual U-Net

Zhang, Zhengxin; Liu, Qingjie; Wang, Yunlong

doi:10.1109/lgrs.2018.2802944

Cited by 1,883 publications

(1,078 citation statements)

References 16 publications

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“…). Compared with Zhang et al 30 ,. we do not employ batch‐normalization, as it would introduce an unwanted scaling of the scatter.…”

Section: Methodsmentioning

confidence: 77%

“…The second part (~(1ÀK)) was modeled using a residual UNet. 29,30 The latter models the low-frequency deviations on different resolution levels, with an increasing number of channels or "features". We used six levels, with 8, 16, 32, 64, 128, and 256 features ( Fig.…”

Section: C Network Design and Trainingmentioning

confidence: 99%

“…3). Compared with Zhang et al 30 , we do not employ batch-normalization, as it would introduce an unwanted scaling of the scatter. Both networks use 3 9 3 convolution kernels, and employ the parametric rectified linear unit (PReLU) 31 as the activation function.…”

Section: C Network Design and Trainingmentioning

confidence: 99%

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ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

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Using a deep convolutional neural network for CBCT intensity correction was shown to be feasible in the pelvic region for the first time. Dose calculation accuracy on CBCT was high for VMAT, but unsatisfactory for IMPT. With respect to the reference technique (CBCT ), the neural network enabled a considerable increase in speed for intensity correction and might eventually allow for on-the-fly shading correction during CBCT acquisition.

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“…). Compared with Zhang et al 30 ,. we do not employ batch‐normalization, as it would introduce an unwanted scaling of the scatter.…”

Section: Methodsmentioning

confidence: 77%

Section: C Network Design and Trainingmentioning

confidence: 99%

See 1 more Smart Citation

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

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“…FCN transforms a segmentation task into a pixel‐level classification task and opens a new method of image semantic segmentation based on deep convolutional networks. FCN has established a new advanced technology in the semantic segmentation of aeronautical optical images and successfully applied to satellite SAR images. Yao et al .…”

Section: Related Workmentioning

confidence: 99%

“…FCN transforms a segmentation task into a pixel-level classification task and opens a new method of image semantic segmentation based on deep convolutional networks. FCN has established a new advanced technology in the semantic segmentation of aeronautical optical images [9] and successfully applied to satellite SAR images. Yao et al [10] used pretrained FCNs on SAR images to classify buildings, land use, bodies of water and other natural areas, but unsatisfactory results are obtained for buildings.…”

Section: Spatial Information Deep Cnns (Dcnns) Havementioning

confidence: 99%

A segmentation network with multiattention and its application to SAR image analysis

Long

Mabu

2020

IEEJ Transactions Elec Engng

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Image segmentation plays an important role in image understanding and region‐based applications. Many image segmentation algorithms have been proposed, but in this paper, we enhance the segmentation performance of deep learning using attention models that extract important features from the target images. The structure of the segmentation network is an encoder–decoder model that can combine position features and channel features, where the attention mechanisms refine both position and channel features. In the experiments, the proposed method is applied to satellite image analysis, where synthetic aperture radar images are analyzed to detect landslide areas after heavy rain occurred. The experimental results show that the proposed method obtains higher segmentation accuracy comparing with some conventional methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening

Wang

Zhang

et al. 2021

JAMA Netw Open

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Key Points Question Can deep learning algorithms achieve a performance comparable with that of ophthalmologists on multidimensional identification of retinopathy of prematurity (ROP) using wide-field retinal images? Findings In this diagnostic study of 14 108 eyes of 8652 preterm infants, a deep learning–based ROP screening platform could identify retinal images using 5 classifiers, including image quality, stages of ROP, intraocular hemorrhage, preplus/plus disease, and posterior retina. The platform achieved an area under the curve of 0.983 to 0.998, and the referral system achieved an area under the curve of 0.9901 to 0.9956; the platform achieved a Cohen κ of 0.86 to 0.98 compared with 0.93 to 0.98 by the ROP experts. Meaning Results suggest that a deep learning platform could identify and classify multidimensional ROP pathological lesions in retinal images with high accuracy and could be suitable for routine ROP screening in general and children’s hospitals.

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Road Extraction by Deep Residual U-Net

Cited by 1,883 publications

References 16 publications

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

A segmentation network with multiattention and its application to SAR image analysis

Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening

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