scholarly journals WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

2021 ◽  
Author(s):  
P Kiran Rao ◽  
Subarna Chatterjee ◽  
M Sreedhar Sha
Keyword(s):  
Computational Complexity ◽  
Network Models ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Small Scale ◽  
Kidney Tumour ◽  
Tumour Segmentation ◽  
Proposed Model ◽  
Potential Applicability ◽  
Weight Pruning

Abstract Background Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation. Methods We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results We trained and evaluated the model with CT images from 300 patients. Thefindings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

scholarly journals WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

2021 ◽  
Author(s):  
P Kiran Rao ◽  
Subarna Chatterjee ◽  
M Sreedhar Sha
Keyword(s):  
Computational Complexity ◽  
Network Models ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Small Scale ◽  
Kidney Tumour ◽  
Tumour Segmentation ◽  
Proposed Model ◽  
Potential Applicability ◽  
Weight Pruning

Abstract Background: Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation.Methods: We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.Results: We trained and evaluated the model with CT images from 300 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

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.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

A Greenwood-Williamson Model of Small-Scale Friction

2005 ◽  
Vol 74 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Reese E. Jones
Keyword(s):  
Numerical Method ◽  
Recursion Formula ◽  
Three Dimensional ◽  
Interfacial Friction ◽  
Small Scale ◽  
Analytical Estimate ◽  
Normal Loading ◽  
Proposed Model ◽  
Loading Case ◽  
Transition Length

A Greenwood and Williamson based model for interfacial friction is presented that incorporates the presliding transition phenomenon that can significantly affect small devices. This work builds on previous similar models by developing: an analytical estimate of the transition length in terms of material and surface parameters, a general recursion formula for the case of slip in one direction with multiple reversals and constant normal loading, and a numerical method for the general three-dimensional loading case. In addition, the proposed model is developed within a plasticity-like framework and is shown to have qualitative similarities with published experimental observations. A number of model problems illustrate the response of the proposed model to various loading conditions.

scholarly journals ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding

2018 ◽  
Vol 4 (10) ◽  
pp. 116 ◽  
Author(s):  
Robail Yasrab
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Substantial Reduction ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Research Area ◽  
Smart Systems ◽  
Proposed Model ◽  
Driving Assistance ◽  
Flexible Architecture

This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer model, while the decoder network uses 16 upsampling and deconvolution units. The encoder of the network has a very flexible architecture that can be altered and trained for any size and resolution of images. The decoder network upsamples and maps the low-resolution encoder’s features. Consequently, there is a substantial reduction in the trainable parameters, as the network recycles the encoder’s pooling indices for pixel-wise classification and segmentation. The proposed model is intended to offer a simplified CNN model with less overhead and higher performance. The network is trained and tested on the famous road scenes dataset CamVid and offers outstanding outcomes in comparison to similar early approaches like FCN and VGG16 in terms of performance vs. trainable parameters.

scholarly journals A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

2018 ◽  
Author(s):  
Feng Jie Zheng ◽  
Fu Zheng Qu ◽  
Xue Guan Song
Keyword(s):  
Pressure Fluctuation ◽  
Large Scale ◽  
Three Dimensional ◽  
Industrial Process ◽  
Multiscale Model ◽  
Computational Time ◽  
Small Scale ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Proposed Model

Reservoir-pipe-valve (RPV) systems are widely used in many industrial process. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operation such as rapid valve opening/closing. To investigate the pressure especially the pressure fluctuation in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled by a zero-dimensional virtual point, the pipe is modeled by a one-dimensional MOC, and the valve is modeled by a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted, in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve are obtained. The results show that the proposed model is in good agreement with the full CFD model in both large-scale and small-scale spaces. Moreover, the proposed model is more computationally efficient than the CFD model, which provides a feasibility in the analysis of complex RPV system within an affordable computational time.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Feng Jie Zheng ◽  
Chao Yong Zong ◽  
William Dempster ◽  
Fu Zheng Qu ◽  
Xue Guan Song
Keyword(s):  
Large Scale ◽  
Pressure Fluctuations ◽  
Three Dimensional ◽  
Multiscale Model ◽  
Computational Time ◽  
Small Scale ◽  
Dimensional System ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Proposed Model

Reservoir-pipe-valve (RPV) systems are widely used in many industrial processes. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operations such as rapid valve opening or closing. To investigate the pressure response, with particular interest in the pressure fluctuations in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled as a zero-dimensional virtual point, the pipe is modeled as a one-dimensional system using the MOC, and the valve is modeled using a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve is obtained. The results show that the proposed model is in good agreement when compared with a high fidelity CFD model used to represent both large-scale and small-scale spaces. As expected, the proposed model is significantly more computationally efficient than the CFD model. This demonstrates the feasibility of analyzing complex RPV systems within an affordable computational time.

scholarly journals IBPS: iBeacons Based Improved Indoor Positioning System

2020 ◽  
Vol 8 (5) ◽  
pp. 3792-3797
Keyword(s):  
Research Work ◽  
Energy Resource ◽  
Smart Phone ◽  
Indoor Positioning ◽  
Small Scale ◽  
Positioning System ◽  
Human Organ ◽  
Proposed Model ◽  
Small Hand ◽  
Indoor Positioning System

Smartphone plays a key role in integrating the entire world into a small hand. This feature made these smartphones as another human organ of many people. One of the main feature in every smart phone is GPS which used to travel new places, to locate and find optimized way to reach their destination. As we aware GPS is an outdoor application, GPS location is not accurate in indoor and small scale areas. This leads to an advanced research to improve the accuracy in GPS positing for the benefit of indoor applications. This research proposes a new iBeacons based Improved Indoor Positioning System for indoor positing application using Bluetooth low energy (BLE) beacons. This model helps the mobile application to find the exact location at micro-level scale. The objective of this research work is to design a potable indoor positing system (IPS) for indoor applications with at least 100m accuracy with in the inbuilt energy resource limitations. The proposed model has been built and verified in all the aspects. The location accuracy and energy efficiency of the proposed model is compared and found better than the existing models

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