Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free Remission in Chronic Myeloid Leukemia.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Same author

Decoding the Myocardium: Tracer-Aware Deep Learning for Patient-Level Classification in Stress-Rest SPECT Myocardial Perfusion Imaging.

Diagnostics (Basel, Switzerland)·2026

Same author

Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling.

Nature medicine·2026

Same author

A morphology-driven proof-of-concept study linking interphase nuclear abnormalities to dicentric-mediated genomic instability.

Annales de biologie clinique·2026

Same author

Prediction of bone marrow fibrosis from complete blood count in myeloproliferative neoplasms (FIBOM-AI): a multicentre machine learning model development and validation study.

The Lancet. Haematology·2026

Same author

Efficacy, safety, and biomarker analysis of datopotamab deruxtecan in advanced non-small cell lung cancer: ICARUS-LUNG01 phase 2 study.

Cancer cell·2026

Search research articles

Related Experiment Video

Updated: Nov 29, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis.

Mihir Sahasrabudhe, Pierre Sujobert, Evangelia I Zacharaki

IEEE Journal of Biomedical and Health Informatics

|November 18, 2020

Summary

Deep learning models, combining blood cell images and clinical data, significantly improve lymphocytosis diagnosis. This approach surpasses traditional methods and expert performance, offering robust and accurate diagnostic potential.

More Related Videos

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

Related Experiment Videos

Last Updated: Nov 29, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Multiplexed Fluorescent Immunohistochemical Staining, Imaging, and Analysis in Histological Samples of Lymphoma

Published on: January 9, 2019

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Accurate diagnosis of lymphocytosis is crucial for patient management.
Traditional diagnostic methods may not fully leverage complex data patterns.
Deep learning offers potential for enhanced diagnostic accuracy.

Purpose of the Study:

To develop and evaluate an end-to-end deep learning model for lymphocytosis diagnosis.
To integrate image-based features and clinical attributes for improved diagnostic performance.
To compare the proposed model against existing methods and expert performance.

Main Methods:

A multi-instance convolutional neural network (MIL-CNN) was employed.
A mixture-of-experts (MoE) formulation combined image features and clinical data.
The model was trained end-to-end for lymphocytosis diagnosis.

Main Results:

The MIL-CNN effectively extracted relevant features from blood cell images.
The MoE model demonstrated robust performance and improved diagnostic accuracy.
The proposed method achieved a balanced accuracy of [Formula: see text], outperforming other approaches.

Conclusions:

Deep learning, particularly the proposed MoE MIL-CNN, shows significant promise for lymphocytosis diagnosis.
The model effectively utilizes both image and clinical data for enhanced accuracy.
This approach has potential for clinical application, offering improved diagnostic capabilities.