AIMC Topic: Microscopy
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Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline: Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium-to-large datasets.
BioEssays : news and reviews in molecular, cellular and developmental biology
May 16, 2019
Here, a streamlined, scalable, laboratory approach is discussed that enables medium-to-large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a...
Automatic segmentation of Sperm's parts in microscopic images of human semen smears using concatenated learning approaches.
Computers in biology and medicine
Apr 30, 2019
Accurate segmentation of the sperms in microscopic semen smear images is a prerequisite step in automatic sperm morphology analysis. It is a challenging task due to the non-uniform distribution of light in semen smear images, low contrast between spe...
Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Apr 22, 2019
Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to cor...
Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.
Journal of biophotonics
Apr 10, 2019
Antimicrobial drugs have an important role in controlling bacterial infectious diseases. However, the increasing resistance of bacteria to antibiotics has become a global health care problem. Rapid determination of antimicrobial susceptibility of cli...
Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.
Sensors (Basel, Switzerland)
Mar 26, 2019
The nematode is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and r...
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.
IEEE transactions on medical imaging
Mar 25, 2019
Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural netw...
Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.
IEEE journal of biomedical and health informatics
Mar 18, 2019
Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and...
Automated segmentation of the corneal endothelium in a large set of 'real-world' specular microscopy images using the U-Net architecture.
Scientific reports
Mar 18, 2019
Monitoring the density of corneal endothelial cells (CEC) is essential in the management of corneal diseases. Its manual calculation is time consuming and prone to errors. U-Net, a neural network for biomedical image segmentation, has shown promising...
Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.
Stem cell reports
Mar 14, 2019
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with...
Deep learning-based super-resolution in coherent imaging systems.
Scientific reports
Mar 8, 2019
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-lim...
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