scholarly journals Use of deep learning for structural analysis of computer tomography images of soil samples

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Ralf Wieland ◽  
Chinatsu Ukawa ◽  
Monika Joschko ◽  
Adrian Krolczyk ◽  
Guido Fritsch ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Structural Analysis ◽  
Computer Tomography ◽  
Ct Images ◽  
Soil Samples ◽  
Automated Annotation ◽  
Medical Computer ◽  
Gradient Based ◽  
Activation Mapping

Soil samples from several European countries were scanned using medical computer tomography (CT) device and are now available as CT images. The analysis of these samples was carried out using deep learning methods. For this purpose, a VGG16 network was trained with the CT images (X). For the annotation (y) a new method for automated annotation, ‘surrogate’ learning, was introduced. The generated neural networks (NNs) were subjected to a detailed analysis. Among other things, transfer learning was used to check whether the NN can also be trained to other y-values. Visually, the NN was verified using a gradient-based class activation mapping (grad-CAM) algorithm. These analyses showed that the NN was able to generalize, i.e. to capture the spatial structure of the soil sample. Possible applications of the models are discussed.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenguo Nie ◽  
Haoliang Jiang ◽  
Levent Burak Kara
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Topology Optimization ◽  
Structural Analysis ◽  
Stress Field ◽  
Convolutional Neural Networks ◽  
Von Mises Stress ◽  
Channel Network ◽  
Promising Alternative ◽  
And Topology

Abstract The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multichannel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available.2

scholarly journals Segmentation of Lung Images using Region based Neural Networks

2018 ◽  
Vol 11 (4) ◽  
pp. 2037-2042 ◽  
Author(s):  
Z. Faizal Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Tomography ◽  
Region Growing ◽  
Ct Images ◽  
Second Step ◽  
Lung Region ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Lung Ct

In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

scholarly journals Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13943-13944
Author(s):  
Kira Vinogradova ◽  
Alexandr Dibrov ◽  
Gene Myers
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Wide Range ◽  
Gradient Based ◽  
Activation Mapping

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose seg-grad-cam, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.

scholarly journals Deep forest

2018 ◽  
Vol 6 (1) ◽  
pp. 74-86 ◽  
Author(s):  
Zhi-Hua Zhou ◽  
Ji Feng
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Decision Trees ◽  
Deep Neural Networks ◽  
Machine Learning Techniques ◽  
Model Complexity ◽  
Layer By Layer ◽  
Theoretical Understanding ◽  
Deep Forest ◽  
Gradient Based

Abstract Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines, we conjecture that the success of deep neural networks owes much to three characteristics, i.e. layer-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand, our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.

scholarly journals A New Radiomic Study on Lung CT Images of Patients with Covid-19 using LBP and Deep Learning (Convolutional Neural Networks (CNN))

2020 ◽  
Author(s):  
Hüseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Ct Images ◽  
Gray Level ◽  
Speed Up ◽  
Rgb Images ◽  
Short Time ◽  
Lung Ct

Abstract The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, the amount of 25 lung CT images in total (15 of Covid-19 patients and 10 of normal) was multiplied (250 images in total) using three data augmentation methods which relate to contrast change, brightness change and noise addition, and these images were subjected to automatic classification. Within the scope of the study, experiments were made for each case which include the use of the CT images of lungs (gray-level and RGB) directly, the images obtained by applying Local Binary Pattern (LBP) to these images (gray-level and RGB) and the images obtained by combining these images (gray-level and RGB). In the study, a 23-layer Convolutional Neural Networks (CNN) architecture was developed and used in classification processes. Leave-one-group-out cross validation method was used to test the proposed system. In this context, the result of the study indicated that the best AUC and EER values were obtained for the combination of original (RGB) and LBP applied (RGB) images, and these figures are 0,9811 and 0,0445 respectively. It was observed that, applying LBP to images, the use of CNN input causes an increase in sensitivity values while it causes a decrease in values of specificity. The highest sensitivity was obtained for the case of using LBP-applied (RGB) images and has a value of 0,9947. Within the scope of the study, the highest values of specificity and accuracy were obtained by the help of CT of lungs (gray-level) with 0,9120 and 95,32%, respectively. The results of the study indicate that analyzing images of lung CT using deep learning methods in diagnosing Covid-19 disease will speed up the diagnosis and significantly reduce the burden on healthcare workers.

scholarly journals Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Zhenguo Nie ◽  
Haoliang Jiang ◽  
Levent Burak Kara
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Topology Optimization ◽  
Structural Analysis ◽  
Stress Field ◽  
Convolutional Neural Networks ◽  
Von Mises Stress ◽  
Channel Network ◽  
Promising Alternative ◽  
And Topology

Abstract The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNN). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multi-channel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available at https://github.com/zhenguonie/stress_net.

scholarly journals Deep learning models for COVID-19 infected area segmentation in CT images

2020 ◽  
Author(s):  
Athanasios Voulodimos ◽  
Eftychios Protopapadakis ◽  
Iason Katsamenis ◽  
Anastasios Doulamis ◽  
Nikolaos Doulamis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Learning Models ◽  
Fully Convolutional Neural Networks ◽  
Infected Area ◽  
Ct Data

Recent studies indicated that detecting radiographic patterns on CT chest scans could in some cases yield higher sensitivity and specificity for COVID-19 detection compared to other methods such as RTPCR. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.

scholarly journals EVALUATING VISUAL IMPRESSIONS BASED ON GAZE ANALYSIS AND DEEP LEARNING: A CASE STUDY OF ATTRACTIVENESS EVALUATION OF STREETS IN DENSELY BUILT-UP WOODEN RESIDENTIAL AREA

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 887-894
Author(s):  
T. Oki ◽  
S. Kizawa
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Residential Area ◽  
Eye Tracker ◽  
Residential Areas ◽  
Evaluation Result ◽  
The Relationship ◽  
Activation Mapping

Abstract. This paper examines the possibility of impression evaluation based on gaze analysis of subjects and deep learning, using an example of evaluating street attractiveness in densely built-up wooden residential areas. Firstly, the relationship between the subjects' gazing tendency and their evaluation of street image attractiveness is analysed by measuring the subjects' gaze with an eye tracker. Next, we construct a model that can estimate an attractiveness evaluation result using convolutional neural networks (CNNs), combined with the method of gradient-weighted class activation mapping (Grad-CAM) - these in in visualizing which street components can contribute to evaluating attractiveness. Finally, we discuss the similarity between the subjects' gaze tendencies and activation heatmaps created by Grad-CAM.

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