scholarly journals An Automatic Knee Osteoarthritis Diagnosis Method Based on Deep Learning: Data from the Osteoarthritis Initiative

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yifan Wang ◽  
Xianan Wang ◽  
Tianning Gao ◽  
Le Du ◽  
Wei Liu
Keyword(s):  
Large Scale ◽  
Severity Assessment ◽  
Classification Result ◽  
Large Dataset ◽  
Detection Model ◽  
Semiautomatic Segmentation ◽  
Segmentation Methods ◽  
Diagnosis Method ◽  
Osteoarthritis Initiative ◽  
Learning Data

Osteoarthritis (OA) is the most common form of arthritis. According to the evidence presented on both sides of the knee bones, radiologists assess the severity of OA based on the Kellgren–Lawrence (KL) grading system. Recently, computer-aided methods are proposed to improve the efficiency of OA diagnosis. However, the human interventions required by previous semiautomatic segmentation methods limit the application on large-scale datasets. Moreover, well-known CNN architectures applied to the OA severity assessment do not explore the relations between different local regions. In this work, by integrating the object detection model, YOLO, with the visual transformer into the diagnosis procedure, we reduce human intervention and provide an end-to-end approach to automatic osteoarthritis diagnosis. Our approach correctly segments 95.57% of data at the expense of training on 200 annotated images on a large dataset that contains more than 4500 samples. Furthermore, our classification result improves the accuracy by 2.5% compared to the traditional CNN architectures.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

scholarly journals Development and evaluation of duplex TaqMan real-time PCR assay for detection and differentiation of wide-type and MGF505-2R gene-deleted African swine fever viruses

2020 ◽  
Author(s):  
zhenhua Guo ◽  
Kunpeng Li ◽  
Songlin Qiao ◽  
Xinxin Chen ◽  
Ruiguang Deng ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Large Scale ◽  
Limit Of Detection ◽  
African Swine Fever ◽  
Economic Losses ◽  
Clinical Samples ◽  
Diagnosis Method ◽  
Pcr Method ◽  
And Control

Abstract Background: African swine fever (ASF) is the most important disease to the pigs and cause serious economic losses to the countries with large-scale swine production. Vaccines are recognized as the most useful tool to prevent and control ASF virus (ASFV) infection. Currently, the MGF505 and MGF360 gene-deleted ASFVs or combined with CD2v deletion were confirmed to be the most promising vaccine candidates. Thus, it is essential to develop a diagnosis method to discriminate wide-type strain from the vaccines used.Results: In this study, we established a duplex TaqMan real-time PCR based on the B646L gene and MGF505-2R gene. The sequence alignment showed that the targeted regions of primers and probes are highly conserved in the genotype II ASFVs. The duplex real-time assay can specifically detect B646L and MGF505-2R gene single or simultaneously without cross-reaction with other porcine viruses tested. The limit of detection was 5.8 copies and 3.0 copies for the standard plasmids containing B646L and MGF505-2R genes, respectively. Clinical samples were tested in parallel by duplex real-time PCR and a commercial ASFV detection kit. The detection results of these two assays against B646L gene were well consistent.Conclusion: We successfully developed and evaluated a duplex TaqMan real-time PCR method which can effectively distinguish the wide type and MGF505 gene-deleted ASFVs. It would be a useful tool for the clinical diagnosis and control of ASF.

scholarly journals Curriculum Learning for Natural Answer Generation

2018 ◽  
Author(s):  
Cao Liu ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Full Advantage ◽  
Real World ◽  
Large Scale ◽  
Question Answering ◽  
Generative Models ◽  
The Internet ◽  
Basic Model ◽  
The Arts ◽  
Learning Data

By reason of being able to obtain natural language responses, natural answers are more favored in real-world Question Answering (QA) systems. Generative models learn to automatically generate natural answers from large-scale question answer pairs (QA-pairs). However, they are suffering from the uncontrollable and uneven quality of QA-pairs crawled from the Internet. To address this problem, we propose a curriculum learning based framework for natural answer generation (CL-NAG), which is able to take full advantage of the valuable learning data from a noisy and uneven-quality corpus. Specifically, we employ two practical measures to automatically measure the quality (complexity) of QA-pairs. Based on the measurements, CL-NAG firstly utilizes simple and low-quality QA-pairs to learn a basic model, and then gradually learns to produce better answers with richer contents and more complete syntaxes based on more complex and higher-quality QA-pairs. In this way, all valuable information in the noisy and uneven-quality corpus could be fully exploited. Experiments demonstrate that CL-NAG outperforms the state-of-the-arts, which increases 6.8% and 8.7% in the accuracy for simple and complex questions, respectively.

scholarly journals The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios

2021 ◽  
Vol 11 (21) ◽  
pp. 10366
Author(s):  
César Córcoles ◽  
Germán Cobo ◽  
Ana-Elena Guerrero-Roldán
Keyword(s):  
Learning Process ◽  
Teaching And Learning ◽  
Large Scale ◽  
Real Data ◽  
Small Scale ◽  
E Learning ◽  
Learning Scenarios ◽  
Real Scenario ◽  
Teaching Learning ◽  
Learning Data

A variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. Most of the literature focuses on large scale learning scenarios, such as MOOCs, where videos are watched hundreds or thousands of times. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher education, where videos are watched tens or hundreds of times, and to analyse whether the solution is useful to teachers to improve the learning process. We have deployed it in a real scenario and collected real data. Furthermore, we have processed and presented the data visually to teachers for those scenarios and have collected and analysed their perception of their usefulness. We conclude that the collected data are perceived as useful by teachers to improve the teaching and learning process.

Deep Learning Based Active Monitoring for Anti-collision between Vessels and Bridges

2019 ◽  
Author(s):  
Limu Chen ◽  
Ye Xia ◽  
Dexiong Pan ◽  
Chengbin Wang
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Data Augmentation ◽  
Information Support ◽  
Single Shot ◽  
Active Monitoring ◽  
Detection Model ◽  
Comparison Results

<p>Deep-learning based navigational object detection is discussed with respect to active monitoring system for anti-collision between vessel and bridge. Motion based object detection method widely used in existing anti-collision monitoring systems is incompetent in dealing with complicated and changeable waterway for its limitations in accuracy, robustness and efficiency. The video surveillance system proposed contains six modules, including image acquisition, detection, tracking, prediction, risk evaluation and decision-making, and the detection module is discussed in detail. A vessel-exclusive dataset with tons of image samples is established for neural network training and a SSD (Single Shot MultiBox Detector) based object detection model with both universality and pertinence is generated attributing to tactics of sample filtering, data augmentation and large-scale optimization, which make it capable of stable and intelligent vessel detection. Comparison results with conventional methods indicate that the proposed deep-learning method shows remarkable advantages in robustness, accuracy, efficiency and intelligence. In-situ test is carried out at Songpu Bridge in Shanghai, and the results illustrate that the method is qualified for long-term monitoring and providing information support for further analysis and decision making.</p>

scholarly journals Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

2020 ◽  
Vol 12 (5) ◽  
pp. 821 ◽  
Author(s):  
Shouyi Wang ◽  
Zhigang Xu ◽  
Chengming Zhang ◽  
Jinghan Zhang ◽  
Zhongshan Mu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Random Field ◽  
Large Scale ◽  
Conditional Random Field ◽  
Pixel Classification ◽  
Classification Result ◽  
Edge Pixel ◽  
Remote Sensing Imagery

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai’an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF’s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.

scholarly journals An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 1029
Author(s):  
Xuzhe Duan ◽  
Qingwu Hu ◽  
Pengcheng Zhao ◽  
Shaohua Wang ◽  
Mingyao Ai
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Large Scale ◽  
Weather Conditions ◽  
Spatial Data Analysis ◽  
Vegetation Coverage ◽  
Economic Activities ◽  
Traditional Methods ◽  
Detection Model ◽  
Nighttime Lights

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

scholarly journals Wind Turbine Anomaly Detection Based on SCADA Data Mining

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 751
Author(s):  
Xiaoyuan Liu ◽  
Senxiang Lu ◽  
Yan Ren ◽  
Zhenning Wu
Keyword(s):  
Anomaly Detection ◽  
Wind Turbine ◽  
Supervisory Control ◽  
Detection Method ◽  
Classification Result ◽  
Detection Model ◽  
Scada System ◽  
Silhouette Coefficient ◽  
Abnormal State ◽  
Low Dimensional

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.

A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees

2010 ◽  
Vol 39 ◽  
pp. 449-454
Author(s):  
Jiang Hui Cai ◽  
Wen Jun Meng ◽  
Zhi Mei Chen
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Predicate Logic ◽  
Frequent Pattern ◽  
Rule Mining ◽  
First Order ◽  
Broad Term ◽  
Diagnosis Method

Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.

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