Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

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A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form

Journal of Mechanical Design ◽

10.1115/1.4037309 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 10

Author(s):

Matthew L. Dering ◽

Conrad S. Tucker

Keyword(s):

Deep Learning ◽

Three Dimensional ◽

Quantitative Measure ◽

Digital Design ◽

Learning Approaches ◽

Test Form ◽

Design Concepts ◽

Training Class ◽

Latent Features ◽

Common Architecture

Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as computational fluid dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on three-dimensional (3D) convolutions that predict functional quantities of digital design concepts. This work defines the term functional quantity to mean a quantitative measure of an artifact's ability to perform a function. Several research questions are derived from this work: (i) Are learned 3D convolutions able to accurately calculate these quantities, as measured by rank, magnitude, and accuracy? (ii) What do the latent features (that is, internal values in the model) discovered by this network mean? (iii) Does this work perform better than other deep learning approaches at calculating functional quantities? In the case study, a proposed network design is tested for its ability to predict several functions (sitting, storing liquid, emitting sound, displaying images, and providing conveyance) based on test form classes distinct from training class. This study evaluates several approaches to this problem based on a common architecture, with the best approach achieving F scores of >0.9 in three of the five functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional headwear, which yields an 84.4% accuracy and 0.786 precision.

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Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs

Sensors ◽

10.3390/s19173738 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3738 ◽

Cited By ~ 16

Author(s):

Abozar Nasirahmadi ◽

Barbara Sturm ◽

Sandra Edwards ◽

Knut-Håkan Jeppsson ◽

Anne-Charlotte Olsson ◽

...

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Imaging System ◽

Three Dimensional ◽

Single Shot ◽

Learning Approaches ◽

Two Dimensional ◽

Average Precision ◽

Convolutional Network ◽

Commercial Farms

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93.

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Confocal laser microscopy of impregnated central nervous system tissue

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100102559 ◽

1988 ◽

Vol 46 ◽

pp. 94-95

Author(s):

J.N. Turner ◽

M. Siemens ◽

D. Szarowski ◽

D.N. Collins

Keyword(s):

Electron Microscopy ◽

Central Nervous System ◽

Nervous System ◽

Three Dimensional ◽

Dimensional Structure ◽

Confocal Laser ◽

High Contrast ◽

Central Nervous System Tissue ◽

Tissue Samples ◽

Reflected Light

A classic preparation of central nervous system tissue (CNS) is the Golgi procedure popularized by Cajal. The method is partially specific as only a few cells are impregnated with silver chromate usualy after osmium post fixation. Samples are observable by light (LM) or electron microscopy (EM). However, the impregnation is often so dense that structures are masked in EM, and the osmium background may be undesirable in LM. Gold toning is used for a subtle but high contrast EM preparation, and osmium can be omitted for LM. We are investigating these preparations as part of a study to develop correlative LM and EM (particularly HVEM) methodologies in neurobiology. Confocal light microscopy is particularly useful as the impregnated cells have extensive three-dimensional structure in tissue samples from one to several hundred micrometers thick. Boyde has observed similar preparations in the tandem scanning reflected light microscope (TSRLM).

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

SSRN Electronic Journal ◽

10.2139/ssrn.3411719 ◽

2019 ◽

Author(s):

Qian Wu ◽

Weiling Zhao ◽

Xiaobo Yang ◽

Hua Tan ◽

Lei You ◽

...

Keyword(s):

Risk Factors ◽

Deep Learning ◽

Learning Approaches

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The Problem of Fraudulent Content on the Web: Deep Learning Approaches

SSRN Electronic Journal ◽

10.2139/ssrn.3575411 ◽

2020 ◽

Author(s):

Priyanka Meel ◽

Farhin Bano ◽

Dr. Dinesh K. Vishwakarma

Keyword(s):

Deep Learning ◽

Learning Approaches ◽

The Web

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Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

Medical Physics ◽

10.1002/mp.13581 ◽

2019 ◽

Vol 46 (7) ◽

pp. 3180-3193 ◽

Cited By ~ 13

Author(s):

Ran Zhou ◽

Aaron Fenster ◽

Yujiao Xia ◽

J. David Spence ◽

Mingyue Ding

Keyword(s):

Deep Learning ◽

Three Dimensional ◽

Ultrasound Images

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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