Results 251 to 260 of about 5,768,673 (280)

A U-Net Improved Version for Crop and Weed Segmentation from Aerial Images. [PDF]

Sensors (Basel)
Bunica-Mihai A, Popescu D, Ichim L.
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Microaneurysm segmentation in early diabetic retinopathy: a localized patch-based U-Net approach to overcome microscopic lesion oversight. [PDF]

Int J Retina Vitreous
Atada LD, Manjunath MP, Prasad DK, Srinivasan V.   +3 more
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EPRA U-Net: an efficient pyramid residual attention framework for accurate infarct segmentation in diffusion-weighted MRI. [PDF]

BMC Med Imaging
Ulutas H, Sahin ME, Erkoc MF, Yuce E, Tuncer T, Dogan S, Kiranyaz S.   +6 more
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Urban road surface crack detection based on U-net and ResNeXt network. [PDF]

PLoS One
Qiao J, Wang H, Zhou Z, Meng Y, Gong M.
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PatchGAN-guided semi-supervised U-Net for carotid plaque segmentation in ultrasound under limited annotation conditions. [PDF]

BMC Med Imaging
Ravindran SL, Jadhav D, Sangle S, Jain PK, Nicolaides A, Saba L, Abraham A, Suri JS.   +7 more
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E-Res U-Net: An improved U-Net model for segmentation of muscle images

Expert Systems with Applications, 2021
Abstract In this paper, we propose a new semantic segmentation network called ’E-Res U-Net’, to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm.
Junsheng Zhou, Yiwen Lu, Siyi Tao, Xuan Cheng, Chenxi Huang 0001   +4 more
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Improved segmentation by adversarial u-net

Medical Imaging 2021: Computer-Aided Diagnosis, 2021
Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U–Net that consists of an encoder-decoder ...
David Sriker, Dana Cohen 0002, Noa Cahan, Hayit Greenspan   +3 more
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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
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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
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A variational U‐Net for motion retargeting

Computer Animation and Virtual Worlds, 2020
AbstractMotion retargeting is the process of copying motion from one character (source) to another (target) when the source and target body sizes and proportions (of arms, legs, torso, etc.) are different. The problem of automatic motion retargeting has been studied for several decades; however, the motion quality obtained with the application of ...
Seong Uk Kim, Hanyoung Jang, Jongmin Kim 0005   +2 more
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