scholarly journals A Multilane Tracking Algorithm Using IPDA with Intensity Feature

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 461
Author(s):  
Behzad Akbari ◽  
Jeyan Thiyagalingam ◽  
Richard Lee ◽  
Thia Kirubarajan
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Autonomous Vehicles ◽  
False Negative ◽  
Lane Detection ◽  
Multitarget Tracking ◽  
Lighting Conditions ◽  
Novel Approach ◽  
Alternative Approach ◽  
Consistent Manner

Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as 5%, 12%, and 3% improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers 57%, 31%, 4%, and 9× improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.

scholarly journals Pixel-based machine learning and image reconstitution for dot-ELISA pathogen serodiagnosis

2020 ◽  
Author(s):  
Cleo Anastassopoulou ◽  
Athanasios Tsakris ◽  
George P. Patrinos ◽  
Yiannis Manoussopoulos
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Color Discrimination ◽  
Supervised Machine Learning ◽  
Multivariate Logistic Regression Model ◽  
Enzyme Linked Immunosorbent Assay ◽  
Novel Approach ◽  
Image Pixels ◽  
Diagnostic Applications ◽  
Dot Elisa

AbstractSerological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB (“Red”, “Green”, “Blue”) pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images which would then be reconstituted by pixels having probabilities above a cutoff that may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

scholarly journals The Confirmation Rate of Primary Hits: A Predictive Model

2002 ◽  
Vol 7 (3) ◽  
pp. 175-190 ◽  
Author(s):  
Paul Fogel ◽  
Pascal Collette ◽  
Alain Dupront ◽  
Tina Garyantes ◽  
Denis Guédini
Keyword(s):  
Predictive Model ◽  
False Positive ◽  
False Negative ◽  
Individual Compound ◽  
Additional Advantage ◽  
Primary Screening ◽  
Alternative Approach ◽  
Compound Screening ◽  
Calculated Probability

HTS data from primary screening are usually analyzed by setting a cutoff for activity, in order to minimize both false-negative and false-positive rates. An alternative approach, based on a calculated probability of being active, is presented here. Given the predicted confirmation rate derived from this probability, the number of primary positives selected for follow-up can be optimized to maximize the number of true positives without picking too many false positives. Typical cutoff-determining methods are more serendipitous in their nature and not easily optimized in an effort to optimize screening efforts. An additional advantage of calculating a probability of being active for each compound screened is that orthogonal mixtures can be deconvoluted without presetting a deconvolution threshold. An important consequence of using the probability of being active with orthogonal mixtures is that individual compound screening results can be recorded irrespective of whether the assays were performed on single compounds or on cocktails.

scholarly journals Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

2021 ◽  
Vol 15 (3) ◽  
pp. 1551-1565
Author(s):  
Stephan Paul ◽  
Marcus Huntemann
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
False Positive ◽  
Cloud Cover ◽  
Near Infrared ◽  
False Negative ◽  
Open Water ◽  
Thermal Infrared ◽  
Polar Regions ◽  
Spatial Coverage

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

scholarly journals Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features

2021 ◽  
Vol 15 ◽  
Author(s):  
Urszula Hohmann ◽  
Faramarz Dehghani ◽  
Tim Hohmann
Keyword(s):  
Machine Learning ◽  
Cell Death ◽  
False Positive ◽  
Neuronal Damage ◽  
False Negative ◽  
Neuronal Cell ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Analysis Tool

Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatically lesioned and subsequently treated with potential neuroprotective agents. Thereby cell death is regularly assessed by measuring the propidium iodide (PI) uptake or counting of PI-positive nuclei. The applied methods have a limited applicability, either in terms of objectivity and time consumption or regarding its applicability. Consequently, new tools for analysis are needed. Here, we present a framework to mimic manual counting using machine learning algorithms as tools for semantic segmentation of PI-positive dead cells in hippocampal slice cultures. Therefore, we trained a support vector machine (SVM) to classify images into either “high” or “low” neuronal damage and used naïve Bayes, discriminant analysis, random forest, and a multilayer perceptron (MLP) as classifiers for segmentation of dead cells. In our final models, pixel-wise accuracies of up to 0.97 were achieved using the MLP classifier. Furthermore, a SVM-based post-processing step was introduced to differentiate between false-positive and false-negative detections using morphological features. As only very few false-positive objects and thus training data remained when using the final model, this approach only mildly improved the results. A final object splitting step using Hough transformations was used to account for overlap, leading to a recall of up to 97.6% of the manually assigned PI-positive dead cells. Taken together, we present an analysis tool that can help to objectively and reproducibly analyze neuronal damage in brain-derived slice cultures, taking advantage of the morphology of pycnotic cells for segmentation, object splitting, and identification of false positives.

scholarly journals Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

2021 ◽  
Author(s):  
Philipp Sterner ◽  
David Goretzko ◽  
Florian Pargent
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Binary Classification ◽  
False Negative ◽  
Classification Algorithms ◽  
Practical Application ◽  
Cost Sensitive Learning ◽  
Misclassification Costs ◽  
Mathematical Foundations ◽  
Open Materials

Psychology has seen an increase in machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false-positive or false-negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive learning (CSL) methods can take different cost ratios into account. We present the mathematical foundations and introduce a taxonomy of the most commonly used CSL methods, before demonstrating their application and usefulness on psychological data, i.e., the drug consumption dataset ($N = 1885$) from the UCI Machine Learning Repository. In our example, all demonstrated CSL methods noticeably reduce mean misclassification costs compared to regular ML algorithms. We discuss the necessity for researchers to perform small benchmarks of CSL methods for their own practical application. Thus, our open materials provide R code, demonstrating how CSL methods can be applied within the mlr3 framework (https://osf.io/cvks7/).

scholarly journals A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT

2021 ◽  
Author(s):  
Justin Liu
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Model Performance ◽  
Chest Ct ◽  
Health Crisis ◽  
Fine Grained ◽  
Novel Approach ◽  
Proposed Model ◽  
Ct Data ◽  
Computational Resources

Abstract Background: In a worldwide health crisis as severe as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reversetranscription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. Findings: This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Additionally, image preprocessing algorithms were developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 92.71% and a sensitivity of 95.79%. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls.Conclusions: Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement a radiologist’s decision or serve as an alternative screening tool. Future endeavors could provide insight into infection severity, patient risk stratification, and more precise visualizations

Novel machine-learning based cloud mask and its application for Antarctic polynya monitoring using MODIS thermal-infrared imagery

2021 ◽  
Author(s):  
Stephan Paul ◽  
Marcus Huntemann
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
False Positive ◽  
Cloud Cover ◽  
Near Infrared ◽  
False Negative ◽  
Open Water ◽  
Thermal Infrared ◽  
Spatial Coverage ◽  
Cloud Mask

<p>The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice/open water (false-negative) and ii) open-water/thin-ice areas as clouds (false-positive), which results in an underestimation of actual polynya area and subsequent derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data results in an overall increase of 20% in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44% through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.</p>

scholarly journals An Enhanced Machine Learning Topic Classification Methodology for Cybersecurity

2021 ◽  
Author(s):  
Elijah Pelofske ◽  
Lorie M. Liebrock ◽  
Vincent Urias
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Cross Validation ◽  
Majority Vote ◽  
False Negative ◽  
Learning Models ◽  
Decision Mechanism ◽  
Validation Experiment ◽  
Related Text ◽  
Machine Learning Models

In this research, we use user defined labels from three internet text sources (Reddit, Stackexchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural text. We analyze the false positive and false negative rates of each of the 21 model’s in a cross validation experiment. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as the decision mechanism for detecting cybersecurity related text. We also show that the majority vote mechanism of the CTC tool provides lower false negative and false positive rates on average than any of the 21 individual models. We show that the CTC tool is scalable to the hundreds of thousands of documents with a wall clock time on the order of hours.

scholarly journals Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

2021 ◽  
Vol 12 ◽  
Author(s):  
Cleo Anastassopoulou ◽  
Athanasios Tsakris ◽  
George P. Patrinos ◽  
Yiannis Manoussopoulos
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Color Discrimination ◽  
Supervised Machine Learning ◽  
Multivariate Logistic Regression Model ◽  
Enzyme Linked Immunosorbent Assay ◽  
Novel Approach ◽  
Image Pixels ◽  
Diagnostic Applications ◽  
Dot Elisa

Serological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB (“Red,” “Green,” “Blue”) pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images, which would then be reconstituted by pixels having probabilities above a cutoff. The cutoff may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

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