AIMC Topic: Microscopy
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The Spontaneous Control of HIV Replication is Characterized by Decreased Pathological Changes in the Gut-associated Lymphoid Tissue.
Current HIV research
Jan 1, 2018
BACKGROUND: HIV infection induces alterations in the gut-associated lymphoid tissue (GALT) that constitutes the most important site for viral replication due to the extensive presence of effector memory T-cells. In the case of HIV-controllers, severa...
Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2018
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus...
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
Bioinformatics (Oxford, England)
Aug 1, 2017
SUMMARY: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major...
Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy.
Lab on a chip
Jul 11, 2017
According to WHO, about 10 million new cases of thrombotic disorders are diagnosed worldwide every year. Thrombotic disorders, including atherothrombosis (the leading cause of death in the US and Europe), are induced by occlusion of blood vessels, du...
A multi-scale convolutional neural network for phenotyping high-content cellular images.
Bioinformatics (Oxford, England)
Jul 1, 2017
MOTIVATION: Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustm...
Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.
Optics express
Jun 26, 2017
We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the...
AlphaGo, Deep Learning, and the Future of the Human Microscopist.
Archives of pathology & laboratory medicine
May 1, 2017
Cytopathological image analysis using deep-learning networks in microfluidic microscopy.
Journal of the Optical Society of America. A, Optics, image science, and vision
Jan 1, 2017
Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing pr...
Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning.
Lab on a chip
Nov 1, 2016
Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact an...
Automatic classification of cancer cells in multispectral microscopic images of lymph node samples.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue un...
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