Classification of Cells in Time-Lapse Quantitative Phase Image by Supervised Machine Learning

2019 ◽  
Author(s):  
L. Strbkova ◽  
P. Vesely ◽  
R. Chmelik
Keyword(s):  
Machine Learning ◽  
Time Lapse ◽  
Supervised Machine Learning ◽  
Phase Image ◽  
Quantitative Phase

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249196
Author(s):  
Luke Sheneman ◽  
Gregory Stephanopoulos ◽  
Andreas E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Supervised Machine Learning ◽  
Label Free ◽  
Learning Methods ◽  
Method Performance ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Machine Learning Based Track Classification and Estimation using Kalman Filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1700-1704
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Velocity Model ◽  
Supervised Machine Learning ◽  
Multiple Target ◽  
Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

scholarly journals Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

10.2196/20995 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e20995
Author(s):  
Debbie Rankin ◽  
Michaela Black ◽  
Bronac Flanagan ◽  
Catherine F Hughes ◽  
Adrian Moore ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Function ◽  
Cognitive Performance ◽  
Cognitive Dysfunction ◽  
Service Providers ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Score Range

Background Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. Objective This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. Methods Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. Results In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. Conclusions The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

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