AIMC Topic: Eosinophils

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White blood cells detection and classification based on regional convolutional neural networks.

Medical hypotheses Nov 4, 2019
White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Ba...
Humans Neural Networks, Computer Reproducibility of Results Sensitivity and Specificity Software Algorithms Monocytes Lymphocytes Neutrophils Pattern Recognition, Automated Leukocytes Eosinophils Deep Learning Image Processing, Computer-Assisted Diagnosis, Computer-Assisted
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White blood cells identification system based on convolutional deep neural learning networks.

Computer methods and programs in biomedicine Nov 16, 2017
BACKGROUND AND OBJECTIVES: White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear im...
Eosinophils Lymphocytes Deep Learning Leukocytes Image Processing, Computer-Assisted Monocytes Neural Networks, Computer Medical Informatics Humans Neutrophils Basophils Reproducibility of Results
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Utilizing Deep Learning to Analyze Whole Slide Images of Colonic Biopsies for Associations Between Eosinophil Density and Clinicopathologic Features in Active Ulcerative Colitis.

Inflammatory bowel diseases Mar 30, 2022
BACKGROUND: Eosinophils have been implicated in the pathogenesis of ulcerative colitis and have been associated with disease course and therapeutic response. However, associations between eosinophil density, histologic activity, and clinical features...
Humans Cross-Sectional Studies Biopsy Eosinophils Deep Learning Colitis, Ulcerative
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Label-free optical hemogram of granulocytes enhanced by artificial neural networks.

Optics express May 13, 2019
An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to id...
Holography Cell Lineage Principal Component Analysis Flow Cytometry Eosinophils Humans Cell Separation Spectrum Analysis, Raman Neutrophils Neural Networks, Computer
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Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

Annals of the American Thoracic Society Jan 1, 2018
RATIONALE: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based o...
Female Asthma Male Severity of Illness Index Phenotype Precision Medicine Models, Biological Algorithms Humans Anti-Asthmatic Agents Machine Learning Bronchodilator Agents Child Pediatric Obesity Treatment Outcome Eosinophils
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