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Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy.

Communications biology
Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these...

Red blood cell classification in lensless single random phase encoding using convolutional neural networks.

Optics express
Rapid cell identification is achieved in a compact and field-portable system employing single random phase encoding to record opto-biological signatures of living biological cells of interest. The lensless, 3D-printed system uses a diffuser to encode...

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...

Emerging use of machine learning and advanced technologies to assess red cell quality.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine research. Thus, there is an urgent need to understand the potential adverse effects on red blood cells (RBCs) during pre-transfusion storage. Current ass...

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.

Journal of biomedical optics
SIGNIFICANCE: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase im...

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling m...

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

Journal of biomedical optics
SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell le...

Red blood cell phenotyping from 3D confocal images using artificial neural networks.

PLoS computational biology
The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fin...

Detecting cells rotations for increasing the robustness of cell sizing by impedance measurements, with or without machine learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The Coulter principle is a widespread technique for sizing red blood cells (RBCs) in hematological analyzers. It is based on the monitoring of the electrical perturbations generated by cells passing through a micro-orifice, in which a concentrated el...

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.

IEEE journal of biomedical and health informatics
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting ...