Label-free metabolic clustering through unsupervised pixel classification of multiparametric fluorescent images

2020 ◽  
Author(s):  
Giada Bianchetti ◽  
Fabio Ciccarone ◽  
Maria Rosa Ciriolo ◽  
Marco De Spirito ◽  
Giovambattista Pani ◽  
...  
Keyword(s):  
Label Free ◽  
Pixel Classification

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

scholarly journals Towards automated cancer screening: Label-free classification of fixed cell samples using wavelength modulated Raman spectroscopy

2018 ◽  
Vol 11 (4) ◽  
pp. e201700244 ◽  
Author(s):  
Lana Woolford ◽  
Mingzhou Chen ◽  
Kishan Dholakia ◽  
C. Simon Herrington
Keyword(s):  
Raman Spectroscopy ◽  
Cancer Screening ◽  
Label Free ◽  
Fixed Cell ◽  
Free Classification

Classification of blood cells and tumor cells using label-free ultrasound and photoacoustics

2015 ◽  
Vol 87 (8) ◽  
pp. 741-749 ◽  
Author(s):  
Eric M. Strohm ◽  
Michael C. Kolios
Keyword(s):  
Tumor Cells ◽  
Blood Cells ◽  
Label Free

Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants

The Analyst ◽  
2021 ◽  
Author(s):  
Andrea Barucci ◽  
Cristiano D'Andrea ◽  
Edoardo Farnesi ◽  
Martina Banchelli ◽  
Chiara Amicucci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Label Free ◽  
Structural Determinants ◽  
Machine Learning Classification ◽  
Sers Detection ◽  
Biological Differences

We implement a machine learning classification of similar proteins by PCA mixed with multipeak fitting on SERS spectra for effective discrimination based on valid biological differences.

scholarly journals Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alessio Lugnan ◽  
Emmanuel Gooskens ◽  
Jeremy Vatin ◽  
Joni Dambre ◽  
Peter Bienstman
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Particle Analysis ◽  
Label Free ◽  
Machine Learning Approach ◽  
Microflow Cytometer ◽  
Learning Machine ◽  
Learning Issues ◽  
Low Computational Cost

AbstractMachine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of $${15.2}\,\upmu \text {m}$$ 15.2 μ m and $${18.6}\,\upmu \text {m}$$ 18.6 μ m . To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.

scholarly journals Denoising of digital images through PSO based pixel classification

2013 ◽  
Vol 3 (4) ◽  
Author(s):  
Somnath Mukhopadhyay ◽  
Jyotsna Mandal
Keyword(s):  
Digital Images ◽  
Fitness Function ◽  
Unsupervised Classification ◽  
Noisy Image ◽  
Pixel Classification ◽  
Visual Effects ◽  
Swarm Optimization ◽  
Image Pixels ◽  
Noise Models

AbstractThis paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

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