AIMC Topic: Microscopy
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Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.
Journal of chemical information and modeling
Mar 6, 2019
Predicting the outcome of biological assays based on high-throughput imaging data is a highly promising task in drug discovery since it can tremendously increase hit rates and suggest novel chemical scaffolds. However, end-to-end learning with convol...
Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.
Scientific reports
Mar 5, 2019
Semen quality assessed by sperm count and sperm cell characteristics such as morphology and motility, is considered to be the main determinant of men's reproductive health. Therefore, sperm cell selection is vital in assisted reproductive technology ...
Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.
PloS one
Feb 27, 2019
Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is key to full patient recovery. The Microscopic Observed Drug Susceptibility (MODS) is a test to diagnos...
Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.
Journal of medical systems
Feb 23, 2019
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization,...
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.
IEEE transactions on bio-medical engineering
Feb 22, 2019
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...
An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks.
Microscopy research and technique
Feb 11, 2019
Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time-consum...
Detection of Extended-Spectrum β-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.
Analytical chemistry
Jan 25, 2019
The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria is extended-spectrum β-lactamase-producing bacteria (ESBL-positive = ESBL). Due to widespread and c...
Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.
Genetic epidemiology
Jan 4, 2019
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environment...
Deep Learning in Image Cytometry: A Review.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Dec 19, 2018
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is ap...
Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study.
Head & neck
Dec 12, 2018
BACKGROUND: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed.
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