scholarly journals Algorithm for Detecting Characteristic Points on a Three-Dimensional, Whole-Body Human Scan

2020 ◽  
Vol 10 (4) ◽  
pp. 1342
Author(s):  
Michał Koźbiał ◽  
Łukasz Markiewicz ◽  
Robert Sitnik
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Characteristic Point ◽  
Three Dimensional ◽  
Whole Body ◽  
Data Preparation ◽  
Surface Projection ◽  
Characteristic Points ◽  
Novel Method ◽  
One Machine

Anthropometric landmarks obtained from three-dimensional (3D) body scans are widely used in medicine, civil engineering, and virtual reality. For all those fields, an acquisition of certain and accurate landmark positions is crucial for obtaining satisfying results. Manual marking is time-consuming and is affected by the subjectivity of the human operator. Therefore, an automatic approach has become increasingly popular. This paper provides a short survey of different attempts for automatic landmark localization, from which one machine learning-based method was further analyzed and extended in the case of input data preparation for a convolutional neural network (CNN). A novel method of data processing is presented which utilize a mid-surface projection followed by further unwrapping. The article emphasizes its significance and the way it affects the outcome of a deep neural network. The workflow and the detailed description of algorithms used are included in this paper. To validate the method, it was compared with the orthogonal projection used for the state-of-the-art approach. Datasets consisting of 200 specimens, acquired using both methods, were used for convolutional neural networks training and 20 for validation. In this paper, we used YOLO v.3 architecture for detection and ResNet-152 for classification. For each approach, localizations of 22 normalized body landmarks for 10 male and 10 female subjects of different ages and various postures were obtained. To compare the accuracy of approaches, errors and their distribution were measured for each characteristic point. Experiments confirmed that the mid-surface projections resulted, on average, in a 14% accuracy improvement and up to 15% enhancement of resistance on errors related to scan imperfections.

scholarly journals Edge-Nodes Representation Neural Machine for Link Prediction

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 12 ◽  
Author(s):  
Guangluan Xu ◽  
Xiaoke Wang ◽  
Yang Wang ◽  
Daoyu Lin ◽  
Xian Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Formation Mechanism ◽  
Link Prediction ◽  
State Of The Art ◽  
Prediction Methods ◽  
Topological Features ◽  
Novel Method ◽  
Fully Connected ◽  
Two Sides

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics.

A novel method of three-dimensional localization based on a neural network algorithm

2009 ◽  
Vol 33 (3) ◽  
pp. 192-198 ◽  
Author(s):  
X. Guo ◽  
G. Yan ◽  
W. He
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Novel Method

scholarly journals A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 493
Author(s):  
Charis Ntakolia ◽  
Dimitrios E. Diamantis ◽  
Nikolaos Papandrianos ◽  
Serafeim Moustakidis ◽  
Elpiniki I. Papageorgiou
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Bone Metastasis ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Cancer Metastasis ◽  
State Of The Art ◽  
Metastatic Cancer ◽  
Whole Body ◽  
Free Parameters

Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.

scholarly journals A protection method of trained CNN model with a secret key from unauthorized access

2021 ◽  
Vol 10 ◽  
Author(s):  
AprilPyone Maungmaung ◽  
Hitoshi Kiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Network Models ◽  
The State ◽  
Secret Key ◽  
Neural Network Models ◽  
Unauthorized Access ◽  
Protection Method ◽  
Novel Method

In this paper, we propose a novel method for protecting convolutional neural network models with a secret key set so that unauthorized users without the correct key set cannot access trained models. The method enables us to protect not only from copyright infringement but also the functionality of a model from unauthorized access without any noticeable overhead. We introduce three block-wise transformations with a secret key set to generate learnable transformed images: pixel shuffling, negative/positive transformation, and format-preserving Feistel-based encryption. Protected models are trained by using transformed images. The results of experiments with the CIFAR and ImageNet datasets show that the performance of a protected model was close to that of non-protected models when the key set was correct, while the accuracy severely dropped when an incorrect key set was given. The protected model was also demonstrated to be robust against various attacks. Compared with the state-of-the-art model protection with passports, the proposed method does not have any additional layers in the network, and therefore, there is no overhead during training and inference processes.

Structure modeling in three-dimensional simulation of weft-knitted seamless kneepads

2021 ◽  
pp. 004051752110408
Author(s):  
Yingle Shen ◽  
Zhijia Dong ◽  
Honglian Cong
Keyword(s):  
Real Time ◽  
High Efficiency ◽  
Characteristic Point ◽  
Three Dimensional ◽  
Simulation Method ◽  
Loop Model ◽  
Characteristic Points ◽  
Dimensional Simulation ◽  
Joint Programming ◽  
Basic Loop

This paper proposes a simulation method suitable for weft-knitted seamless kneepads. The purpose of this study is to realize the simulation of complete weft-knitted products, and it has the advantage of speed to display the simulation results online in real-time. The loop of the basic loop model is controlled by up to eight characteristic points and the yarn path is fitted by a three-dimensional spline curve. To string the loop model into the fabric, the coordinate of the characteristic point of the loop at other positions is obtained based on the loop model at the origin by using the translation matrix and the rotation matrix. The simulation of the weft-knitted kneepad is realized by the joint programming of Visual Studio and WebGL. The results show that the structure model can achieve the simulation of the kneepad with high efficiency. This method has strong real-time performance and practicability.

scholarly journals Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 385 ◽  
Author(s):  
Ying Chen ◽  
Zhihao Zhang ◽  
Lei Zhong ◽  
Tong Chen ◽  
Juxiang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Near Infrared ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Expression Recognition ◽  
Facial Expression Database

Near-infrared (NIR) facial expression recognition is resistant to illumination change. In this paper, we propose a three-stream three-dimensional convolution neural network with a squeeze-and-excitation (SE) block for NIR facial expression recognition. We fed each stream with different local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to different local features to further improve recognition accuracy. The experimental results on the Oulu-CASIA NIR facial expression database showed that the proposed method has a higher recognition rate than some state-of-the-art algorithms.

scholarly journals Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases

2021 ◽  
Vol 15 ◽  
Author(s):  
Saba Momeni ◽  
Amir Fazlollahi ◽  
Leo Lebrat ◽  
Paul Yates ◽  
Christopher Rowe ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
State Of The Art ◽  
Three Dimensional ◽  
Ground Truth ◽  
Imaging Features ◽  
Neural Network Classifier ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
High Diversity

Cerebral microbleeds (CMB) are increasingly present with aging and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI, such as susceptibility imaging, but are challenging to train because of the limited availability of ground truth and many confounding imaging features, such as vessels or infarcts. In this study, we present a novel generative adversarial network (GAN) that has been trained to generate three-dimensional lesions, conditioned by volume and location. This allows one to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state-of-the-art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high diversity and without needing laboriously marked ground truth.

scholarly journals UNIQ

2021 ◽  
Vol 37 (1--4) ◽  
pp. 1-15
Author(s):  
Chaim Baskin ◽  
Natan Liss ◽  
Eli Schwartz ◽  
Evgenii Zheltonozhskii ◽  
Raja Giryes ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Low Complexity ◽  
High Accuracy ◽  
Trade Off ◽  
Training Time ◽  
Uniform Quantization ◽  
Novel Method ◽  
A Minor ◽  
Prior State

We present a novel method for neural network quantization. Our method, named UNIQ , emulates a non-uniform k -quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform quantization approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We further propose a novel complexity metric of number of bit operations performed (BOPs), and we show that this metric has a linear relation with logic utilization and power. We suggest evaluating the trade-off of accuracy vs. complexity (BOPs). The proposed method, when evaluated on ResNet18/34/50 and MobileNet on ImageNet, outperforms the prior state of the art both in the low-complexity regime and the high accuracy regime. We demonstrate the practical applicability of this approach, by implementing our non-uniformly quantized CNN on FPGA.

scholarly journals Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge

2021 ◽  
Vol 21 (S7) ◽  
Author(s):  
Tao Li ◽  
Ying Xiong ◽  
Xiaolong Wang ◽  
Qingcai Chen ◽  
Buzhou Tang
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
State Of The Art ◽  
Relation Extraction ◽  
Oriented Graph ◽  
Document Structure ◽  
External Knowledge ◽  
Novel Method ◽  
Public Datasets ◽  
Document Level

Abstract Objective Relation extraction (RE) is a fundamental task of natural language processing, which always draws plenty of attention from researchers, especially RE at the document-level. We aim to explore an effective novel method for document-level medical relation extraction. Methods We propose a novel edge-oriented graph neural network based on document structure and external knowledge for document-level medical RE, called SKEoG. This network has the ability to take full advantage of document structure and external knowledge. Results We evaluate SKEoG on two public datasets, that is, Chemical-Disease Relation (CDR) dataset and Chemical Reactions dataset (CHR) dataset, by comparing it with other state-of-the-art methods. SKEoG achieves the highest F1-score of 70.7 on the CDR dataset and F1-score of 91.4 on the CHR dataset. Conclusion The proposed SKEoG method achieves new state-of-the-art performance. Both document structure and external knowledge can bring performance improvement in the EoG framework. Selecting proper methods for knowledge node representation is also very important.

scholarly journals Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1705
Author(s):  
Lamis Hamrouni ◽  
Mohammed Lamine Kherfi ◽  
Oussama Aiadi ◽  
Abdellah Benbelghit
Keyword(s):  
Neural Network ◽  
Classification Scheme ◽  
State Of The Art ◽  
Hierarchical Classification ◽  
Test Image ◽  
Plant Leaves ◽  
Unified Framework ◽  
Classification Schemes ◽  
Learning Classifier ◽  
Novel Method

In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.

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