True Negative Rate

2013 ◽  
pp. 3187-3187
Author(s):  
Daniel Campbell ◽  
Corey Ray-Subramanian ◽  
Winifred Schultz-Krohn ◽  
Kristen M. Powers ◽  
Renee Watling ◽  
...  
Keyword(s):  
True Negative ◽  
True Negative Rate ◽  
Negative Rate

True Negative Rate

2011 ◽  
pp. 999-999
Author(s):  
William Uther ◽  
Dunja Mladenić ◽  
Massimiliano Ciaramita ◽  
Bettina Berendt ◽  
Aleksander Kołcz ◽  
...  
Keyword(s):  
True Negative ◽  
True Negative Rate ◽  
Negative Rate

Incorporation of relative cerebral blood flow into CT perfusion maps reduces false ’at risk' penumbra

2017 ◽  
Vol 10 (7) ◽  
pp. 657-662 ◽  
Author(s):  
Shlomi Peretz ◽  
David Orion ◽  
David Last ◽  
Yael Mardor ◽  
Yotam Kimmel ◽  
...  
Keyword(s):  
At Risk ◽  
Acute Stroke ◽  
Ct Perfusion ◽  
False Negative ◽  
Stroke Patients ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Perfusion Maps

PurposeThe region defined as ‘at risk’ penumbra by current CT perfusion (CTP) maps is largely overestimated. We aimed to quantitate the portion of true ‘at risk’ tissue within CTP penumbra and to determine the parameter and threshold that would optimally distinguish it from false ‘at risk’ tissue, that is, benign oligaemia.MethodsAmong acute stroke patients evaluated by multimodal CT (NCCT/CTA/CTP) we identified those that had not undergone endovascular/thrombolytic treatment and had follow-up NCCT. Maps of absolute and relative CBF, CBV, MTT, TTP and Tmax as well as summary maps depicting infarcted and penumbral regions were generated using the Intellispace Portal (Philips Healthcare, Best, Netherlands). Follow-up CT was automatically co-registered to the CTP scan and the final infarct region was manually outlined. Perfusion parameters were systematically analysed – the parameter that resulted in the highest true-negative-rate (ie, proportion of benign oligaemia correctly identified) at a fixed, clinically relevant false-negative-rate (ie, proportion of ‘missed’ infarct) of 15%, was chosen as optimal. It was then re-applied to the CTP data to produce corrected perfusion maps.ResultsForty seven acute stroke patients met selection criteria. Average portion of infarcted tissue within CTP penumbra was 15%±2.2%. Relative CBF at a threshold of 0.65 yielded the highest average true-negative-rate (48%), enabling reduction of the false ‘at risk’ penumbral region by ~half.ConclusionsApplying a relative CBF threshold on relative MTT-based CTP maps can significantly reduce false ‘at risk’ penumbra. This step may help to avoid unnecessary endovascular interventions.

scholarly journals Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qingkun Meng ◽  
Chao Feng ◽  
Bin Zhang ◽  
Chaojing Tang
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Buffer Overflow ◽  
Analysis Method ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Training Samples ◽  
Extended Code ◽  
Code Property

Buffer overflow vulnerability is a kind of consequence in which programmers’ intentions are not implemented correctly. In this paper, a static analysis method based on machine learning is proposed to assist in auditing buffer overflow vulnerabilities. First, an extended code property graph is constructed from the source code to extract seven kinds of static attributes, which are used to describe buffer properties. After embedding these attributes into a vector space, five frequently used machine learning algorithms are employed to classify the functions into suspicious vulnerable functions and secure ones. The five classifiers reached an average recall of 83.5%, average true negative rate of 85.9%, a best recall of 96.6%, and a best true negative rate of 91.4%. Due to the imbalance of the training samples, the average precision of the classifiers is 68.9% and the average F1 score is 75.2%. When the classifiers were applied to a new program, our method could reduce the false positive to 1/12 compared to Flawfinder.

Mastoid, middle ear and inner ear analysis in CT scan – a possible contribution for the identification of remains

2020 ◽  
Vol 60 (2) ◽  
pp. 102-111
Author(s):  
Henrique Rodrigues ◽  
Rosa Ramos ◽  
Leoni Fagundes ◽  
Orlando Galego ◽  
David Navega ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Visual Assessment ◽  
True Positive Rate ◽  
Observer Agreement ◽  
True Positive ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Objective We aimed to evaluate whether the internal structures of the human ear have anatomical characteristics that are sufficiently distinctive to contribute to human identification and use in a forensic context. Materials and methods After data anonymisation, a dataset containing temporal bone CT scans of 100 subjects was processed by a radiologist who was not involved in the study. Four reference images were selected for each subject. Of the original sample, 10 examinations were used for visual comparison, case by case, against the dataset of 100 patients. This visual assessment was performed independently by four observers, who evaluated the anatomical agreement using a Likert scale (1–5). Inter-observer agreement, true positive rate, positive predictive value, true negative rate, negative predictive value, false positive rate, false negative rate and positive likelihood ratio (LR+) were evaluated. Results Inter-observer agreement obtained an overall Cohen’s Kappa = 99.59%. True positive rate, positive predictive value, true negative rate and negative predictive value were all 100%. Conclusion Visual assessment of the mastoid examinations was shown to be a robust and reliable approach to identify unique osseous features and contribute to human identification. The statistical analysis indicates that regardless of the examiner’s background and training, the approach has a high degree of accuracy.

scholarly journals DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI

2021 ◽  
Author(s):  
Vladimir Fonov ◽  
Mahsa Dadar ◽  
D. Louis Collins ◽  
◽  
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Deep Learning ◽  
Human Brain ◽  
Brain Mri ◽  
True Negative ◽  
Available N ◽  
True Negative Rate ◽  
Negative Rate ◽  
Mri Scans

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

A NEW BINARY SUPPORT VECTOR SYSTEM FOR INCREASING DETECTION RATE OF CREDIT CARD FRAUD

2006 ◽  
Vol 20 (02) ◽  
pp. 227-239 ◽  
Author(s):  
RONG-CHANG CHEN ◽  
TUNG-SHOU CHEN ◽  
CHIH-CHIANG LIN
Keyword(s):  
Credit Card ◽  
Input Data ◽  
Detection Rate ◽  
Vector System ◽  
Support Vector ◽  
True Negative ◽  
Credit Card Fraud ◽  
True Negative Rate ◽  
Negative Rate ◽  
Support Vectors

Recently, a new personalized model has been developed to prevent credit card fraud. This model is promising; however, there remains some problems. Existing approaches cannot identify well credit card frauds from few data with skewed distributions. This paper proposes to address the problem using a binary support vector system (BSVS). The proposed BSVS is based on the support vectors in the support vector machines (SVM) and the genetic algorithm (GA) is employed to select support vectors. To obtain a high true negative rate, self-organizing mapping (SOM) is first employed to estimate the distribution model of the input data. Then BSVS is used to best train the data according to the input data distribution to obtain a high detection rate. Experimental results show that the proposed BSVS is effective especially for predicting a high true negative rate.

scholarly journals The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1982 ◽  
Author(s):  
Noor Ul Huda ◽  
Bolette D. Hansen ◽  
Rikke Gade ◽  
Thomas B. Moeslund
Keyword(s):  
Transfer Learning ◽  
Privacy Preservation ◽  
False Negative ◽  
False Negative Rate ◽  
Model Adaptation ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Thermal Cameras ◽  
The Impact

Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available.

Fast and Robust Algorithm for Detecting Body Posture Using Wrist-Worn Accelerometers

2020 ◽  
Vol 3 (4) ◽  
pp. 285-293
Author(s):  
Marcin Straczkiewicz ◽  
Nancy W. Glynn ◽  
Vadim Zipunnikov ◽  
Jaroslaw Harezlak
Keyword(s):  
True Positive Rate ◽  
Community Dwelling ◽  
Body Posture ◽  
True Positive ◽  
Standing Posture ◽  
Free Living ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Background: The increasing popularity of wrist-worn accelerometers introduces novel challenges to the research on physical activity and sedentary behavior. Estimation of body posture is one such challenge. Methods: The authors proposed an approach called SedUp to differentiate between sedentary (sitting/lying) and standing postures. SedUp is based on the logistic regression classifier, using the wrist elevation and the motion variability extracted from raw accelerometry data collected on the axis parallel to the forearm. The authors developed and tested our method on data from N = 45 community-dwelling older adults. All subjects wore ActiGraph GT3X+ accelerometers on the left and right wrist, and activPAL was placed on the thigh in the free-living environment for 7 days. ActivPAL provided ground truth about body posture. The authors reported SedUp’s classification accuracy for each wrist separately. Results: Using the data from the left wrist, SedUp estimated the standing posture with median true positive rate = 0.83 and median true negative rate = 0.91. Using the data from the right wrist, SedUp estimated the standing posture with median true positive rate = 0.86 and median true negative rate = 0.93. Conclusions: SedUp provides accurate classification of body posture using wrist-worn accelerometers. The separate validation for each wrist allows for the application of SedUp in a wide spectrum of free-living studies.

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