AIMC Topic: Microscopy, Fluorescence
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Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.
Scientific reports
Sep 1, 2016
In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bo...
Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.
Talanta
Oct 21, 2015
Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is pro...
Building cell models and simulations from microscope images.
Methods (San Diego, Calif.)
Oct 17, 2015
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source s...
Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance.
Journal of immunology research
Oct 4, 2015
Application of personalized medicine requires integration of different data to determine each patient's unique clinical constitution. The automated analysis of medical data is a growing field where different machine learning techniques are used to mi...
A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis.
Analytical chemistry
Aug 12, 2025
The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images...
Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.
Journal of the American Chemical Society
Jul 2, 2025
Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a sample's microenvironment. However, data collection and interpretation are often challenging, and traditional methods ...
The Application of Anisotropically Collapsing Gels, Deep Learning, and Optical Microscopy for Chemical Characterization of Nanoparticles and Nanoplastics.
Langmuir : the ACS journal of surfaces and colloids
Jun 3, 2025
The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl gro...
Multiplexing and Sensing with Fluorescence Lifetime Imaging Microscopy Empowered by Phasor U-Net.
Analytical chemistry
Jun 3, 2025
Fluorescence lifetime imaging microscopy (FLIM) has been widely used as an essential multiplexing and sensing tool in frontier fields such as materials science and life sciences. However, the accuracy of lifetime estimation is compromised by limited ...
Local mean suppression filter for effective background identification in fluorescence images.
Computers in biology and medicine
Jun 1, 2025
We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensi...
Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification.
Nature communications
May 30, 2025
The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell su...
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