scholarly journals Exploring highly reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

AbstractThe digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D-based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

scholarly journals Exploring high reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

Abstract The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

scholarly journals Method for Retrieving Digital Agricultural Text Information Based on Local Matching

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1103
Author(s):  
Yue Song ◽  
Minjuan Wang ◽  
Wanlin Gao
Keyword(s):  
Recall Rate ◽  
Retrieval Method ◽  
Retrieval Time ◽  
Search Results ◽  
Retrieval Efficiency ◽  
Query Tree ◽  
Text Information ◽  
Relationship Of ◽  
The Relationship ◽  
Precision Rate

In order to improve the retrieval results of digital agricultural text information and improve the efficiency of retrieval, the method for searching digital agricultural text information based on local matching is proposed. The agricultural text tree and the query tree are constructed to generate the relationship of ancestor–descendant in the query and map it to the agricultural text. According to the retrieval method of the local matching, the vector retrieval method is used to calculate the digital agricultural text and submit the similarity between the queries. The similarity is sorted from large to small so that the agricultural text tree can output digital agricultural text information in turn. In the case of adding interference information, the recall rate and precision rate of the proposed method are above 99.5%; the average retrieval time is between 4s and 6s, and the average retrieval efficiency is above 99%. The proposed method is more efficient in information retrieval and can obtain comprehensive and accurate search results, which can be used for the rapid retrieval of digital agricultural text information.

scholarly journals SOFIA: A Novel Automated Breast Ultrasound System Used on Patients in the Prone Position: A Pilot Study on Lesion Detection in Comparison to Handheld Grayscale Ultrasound

2018 ◽  
Vol 78 (05) ◽  
pp. 499-505 ◽  
Author(s):  
André Farrokh ◽  
Harika Erdönmez ◽  
Fritz Schäfer ◽  
Nicolai Maass
Keyword(s):  
Prone Position ◽  
Recall Rate ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
The Novel ◽  
Scanning Time ◽  
High Recall Rate ◽  
Study Population ◽  
Sensitivity Specificity ◽  
Automated Breast Ultrasound

Abstract Introduction Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Previous data, however, show a high recall rate with this method due to artifacts. The novel automated breast ultrasound scanner SOFIA scans the breast with the patient in a prone position, resulting in even compression of breast tissue. We present our initial results with this examination method. Material and Methods 63 patients were analyzed using a handheld B-mode ultrasound. In cases of BI-RADS 1, 2 or 5, a SOFIA scan was performed. Sensitivity, specificity and accuracy were calculated. Interobserver agreement was evaluated using Cohenʼs kappa. The duration of the scan was measured for both methods. Results No BI-RADS 5 lesion was missed with SOFIA. The SOFIA had an additional recall rate of 16.67% compared to B-mode ultrasound. The sensitivity, specificity and accuracy of SOFIA was 100, 83.33 and 88.89%, respectively. Cohenʼs kappa showed substantial agreement (κ = 0.769) between examiner 1 (B-mode) and examiner 2 (SOFIA). The mean scan duration for the B-mode system and the SOFIA system was 24.21 minutes and 12.94 minutes, respectively. In four cases, D-cup breasts were not scanned in their entirety. Conclusion No cancer was missed when SOFIA was used in this preselected study population. The scanning time was approximately half of that required for B-mode ultrasound. The additional unnecessary recall rate was 16.67%. Larger D cup-size breasts were difficult to position and resulted in an incomplete image in four cases.

scholarly journals Global peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using point matching algorithms

2016 ◽  
Vol 14 (06) ◽  
pp. 1650032 ◽  
Author(s):  
Beichuan Deng ◽  
Seongho Kim ◽  
Hengguang LI ◽  
Elisabeth Heath ◽  
Xiang Zhang
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Matrix Effects ◽  
Heterogeneous Data ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Two Dimensional ◽  
Point Matching ◽  
Peak Alignment ◽  
Global Comparison

Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC[Formula: see text][Formula: see text][Formula: see text]GC-MS) has been used to analyze multiple samples in a metabolomics study. However, due to some uncontrollable experimental conditions, such as the differences in temperature or pressure, matrix effects on samples and stationary phase degradation, there is always a shift of retention times in the two GC columns between samples. In order to correct the retention time shifts in GC[Formula: see text][Formula: see text][Formula: see text]GC-MS, the peak alignment is a crucial data analysis step to recognize the peaks generated by the same metabolite in different samples. Two approaches have been developed for GC[Formula: see text][Formula: see text][Formula: see text]GC-MS data alignment: profile alignment and peak matching alignment. However, these existing alignment methods are all based on a local alignment, resulting that a peak may not be correctly aligned in a dense chromatographic region where many peaks are present in a small region. False alignment will result in false discovery in the downstream statistical analysis. We, therefore, develop a global comparison-based peak alignment method using point matching algorithm (PMA-PA) for both homogeneous and heterogeneous data. The developed algorithm PMA-PA first extracts feature points (peaks) in the chromatography and then searches globally the matching peaks in the consecutive chromatography by adopting the projection of rigid and nonrigid transformation. PMA-PA is further applied to two real experimental data sets, showing that PMA-PA is a promising peak alignment algorithm for both homogenous and heterogeneous data in terms of [Formula: see text]1 score, although it uses only peak location information.

scholarly journals An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1680
Author(s):  
Chenguang Dai ◽  
Zhenchao Zhang ◽  
Dong Lin
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Recall Rate ◽  
Data Sets ◽  
Building Extraction ◽  
Dense Matching ◽  
Object Based ◽  
Alignment Errors ◽  
Precision Rate

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

An Classifying Approach Based on Fuzzy Cognitive Maps

2014 ◽  
Vol 543-547 ◽  
pp. 1651-1654
Author(s):  
Biao Ma ◽  
Tian Tian Yuan
Keyword(s):  
Cognitive Maps ◽  
Recall Rate ◽  
Fuzzy Cognitive Maps ◽  
Classification Method ◽  
New Classification ◽  
Consumer Reviews ◽  
Online Stores ◽  
Precision Rate

Consumer reviews have been considered as a golden mine which can be used to analysis consumers preferences, attitudes and opinions about products or services or online stores. How to exploit this mine effectively has attracted researchers attention. In this paper, a new classification method based on fuzzy cognitive maps is proposal. The experience results showed the approach is more effective way of classifying Chinese reviews based on calculating the precision rate and the recall rate.

scholarly journals Implementation of Simple Additive Weighting (SAW) Method and Profile Matching for Employee Selection

2021 ◽  
Vol 10 (01) ◽  
pp. 022-029
Author(s):  
Andi Pratomo Wiyono ◽  
Muhammad Aziz Muslim ◽  
Muhammad Aswin
Keyword(s):  
Selection Process ◽  
Confusion Matrix ◽  
Employee Selection ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Profile Matching ◽  
Simple Additive Weighting ◽  
A Company ◽  
Precision Rate ◽  
F Measure

Employees are an important element in a company that determines the progress of a company. With good quality employees in a company, it is easier to achieve desired goals of a company. Conventional (manual) recruitment method is vulnerable to non-technical factors such as frequent duplicate data or invalid data. In such condition, a Decision Support System (DSS) will be helpful in making decision process valid and reliable. In this paper, a Simple Addictive Weighting (SAW) method and Profile Matching were proposed to solve employee selection problem. This research was conducted at UPT Career Development and Entrepreneurship Universitas Brawijaya Malang, using data collected from written test selection in 2019. The effectiveness of both methods is analyzed by means of confusion matrix. SAW method give Accuracy rate of 94.7%, Precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, Profile Matching method obtained the Accuracy rate of 90.4.7%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. From these results, it can be concluded that both methods have a high accuracy value accompanied by a high precision value when used for the selection process. This system can also reduce the bias of the same data very well, as can be seen from the high Recall and F-measure rates.

scholarly journals Webshell Detection Based on Executable Data Characteristics of PHP Code

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zulie Pan ◽  
Yuanchao Chen ◽  
Yu Chen ◽  
Yi Shen ◽  
Xuanzhen Guo
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Remote Access ◽  
Detection Accuracy ◽  
Controlled Experiments ◽  
Data Set ◽  
Detection Model ◽  
Proposed Model ◽  
Text Features ◽  
And Control

A webshell is a malicious backdoor that allows remote access and control to a web server by executing arbitrary commands. The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. The key idea is to combine the executable data characteristics of the PHP code with static text features for webshell classification. To verify the proposed model, we construct a cleaned data set of webshell consisting of 2,917 samples from 17 webshell collection projects and conduct extensive experiments. We have designed three sets of controlled experiments, the results of which show that the accuracy of the three algorithms has reached more than 99.40%, the highest reached 99.66%, the recall rate has been increased by at least 1.8%, the most increased by 6.75%, and the F1 value has increased by 2.02% on average. It not only confirms the efficiency of the grammatical features in webshell detection but also shows that our system significantly outperforms several state-of-the-art rivals in terms of detection accuracy and recall rate.

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