ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2019
Label-Free Identification of White Blood Cells Using Machine Learning.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed, 97 citations in OpenAlex.
- Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential.Microsystems & nanoengineering · 2026Article
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- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Imaging Flow Cytometry in HIV Infection Research: Advantages and Opportunities.Methods and protocols · 2025Review
- Imaging flow cytometry as a novel approach for the diagnosis of heparin-induced thrombocytopenia.British journal of haematology · 2025Article
- Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.Frontiers in veterinary science · 2025Article
- Review
- Deep learning pipeline for automated cell profiling from cyclic imaging.Scientific reports · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.bioRxiv : the preprint server for biology · 2024Article
- Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.Journal of pharmaceutical sciences · 2024Article
- Label-free white blood cells classification using a deep feature fusion neural network.Heliyon · 2024Article
- Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.International journal of molecular sciences · 2024Article
- Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly†.Biology of reproduction · 2024Review
- Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.Scientific reports · 2024Article
- Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.Scientific reports · 2024Article
- PXPermute reveals staining importance in multichannel imaging flow cytometry.Cell reports methods · 2024Article
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
- High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions.Trends in analytical chemistry : TRAC · 2023Article
- On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 5 countries.
Funding
Abstract
White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.