scholarly journals Semantic Segmentation of the Eye With a Lightweight Deep Network and Shape Correction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 131967-131974
Author(s):  
Van Thong Huynh ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee ◽  
Soo-Hyung Kim
Keyword(s):  
Semantic Segmentation ◽  
Deep Network ◽  
Shape Correction

EPNet: Efficient Patch-based Deep Network for Real-Time Semantic Segmentation

2020 ◽  
Author(s):  
Sheshang Degadwala ◽  
Utsho Chakraborty ◽  
Sowrav Saha ◽  
Haimanti Biswas ◽  
Dhairya Vyas
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Deep Network

NDNet: Narrow While Deep Network for Real-Time Semantic Segmentation

2020 ◽  
pp. 1-12
Author(s):  
Zhengeng Yang ◽  
Hongshan Yu ◽  
Qiang Fu ◽  
Wei Sun ◽  
Wenyan Jia ◽  
...  
Keyword(s):  
Real Time ◽  
Semantic Segmentation ◽  
Deep Network

scholarly journals WP-UNet: Weight Pruning U-Net with Depthwise Separable Convolutions for Semantic Segmentation of Kidney Tumors

2021 ◽  
Author(s):  
Patike Kiran Rao ◽  
Subarna Chatterjee
Keyword(s):  
Image Analysis ◽  
Computational Complexity ◽  
Ct Scan ◽  
Network Model ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Kidney Tumor ◽  
Functional Layer ◽  
Deep Network ◽  
Weight Pruning

Abstract Background: The major challenge in medical imaging is to achieve high accuracy output during semantic image segmentation tasks in biomedical imaging while having fewer computational operations and faster inference. It is necessary in medical modalities such as kidney tumor CT scan activities, to assist radiologists. Several previous studies have carried out a complex deep network that requires high computational resources. However, a deep network on semantic segmentation of kidney tumor CT scans with fewer flops and parameters has not yet been evaluated.Methods: This research paper presents a novel network model called Weight Pruning U-Net (WP-UNet) which is extremely fast, compact, and computationally efficient to address this problem with kidney tumor CT scan images as an application. Results We apply the proposed deep network model on the kidney tumor CT scan image dataset on computational devices with limited resources for computing. We build a CNN model with minimum parameters inspired by the commonly adapted U-Net architecture of the deep convolution neural network model for CT scan image analysis by making use of a depthwise separable convolution functional layer in the entire network model. We proposed weight pruning with the depthwise separable and batch normalized UNet model to reach the expected performance and reduce the loss in the process. WP-UNet has 3 major benefits,- : (a) a lightweight model with a smaller size (b) fewer parameters, and (c) a faster assumption time with a less than floating point calculation with computational complexity (FLOPs). WP-UNet was tested on the KiTs challenge Biomedical CT Scan imaging Dataset for kidney tumor semantic segmentation (KiTs), and the results showed that comparable and often better results were obtained by the WP-UNet model compared to the existing state-of-the-art models.Conclusions: Unlike previous assumptions, our findings indicate that the architecture proposed is smaller than U-Net and demands 3x less computational complexity while retaining respectable accuracy results. Moreover it affects kidney tumor medical image analysis and their practical application.

Face Completion Using Semantic Segmentation and Geometric Features

2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno
Keyword(s):  
Semantic Segmentation ◽  
Geometric Features

Development of environment design support mixed reality system capable of environment estimation using deep learning

Impact ◽  
2020 ◽  
Vol 2020 (2) ◽  
pp. 9-11
Author(s):  
Tomohiro Fukuda
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Computer Games ◽  
Construction Projects ◽  
Mixed Reality ◽  
Semantic Segmentation ◽  
Environment Design ◽  
Aviation Training ◽  
Architecture And Design ◽  
World Environment

Mixed reality (MR) is rapidly becoming a vital tool, not just in gaming, but also in education, medicine, construction and environmental management. The term refers to systems in which computer-generated content is superimposed over objects in a real-world environment across one or more sensory modalities. Although most of us have heard of the use of MR in computer games, it also has applications in military and aviation training, as well as tourism, healthcare and more. In addition, it has the potential for use in architecture and design, where buildings can be superimposed in existing locations to render 3D generations of plans. However, one major challenge that remains in MR development is the issue of real-time occlusion. This refers to hiding 3D virtual objects behind real articles. Dr Tomohiro Fukuda, who is based at the Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering at Osaka University in Japan, is an expert in this field. Researchers, led by Dr Tomohiro Fukuda, are tackling the issue of occlusion in MR. They are currently developing a MR system that realises real-time occlusion by harnessing deep learning to achieve an outdoor landscape design simulation using a semantic segmentation technique. This methodology can be used to automatically estimate the visual environment prior to and after construction projects.

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