ArticlePloS one2019
Label-free classification of cells based on supervised machine learning of subcellular structures.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
- MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems.Microsystems & nanoengineering · 2025Article
- Article
- Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.Scientific reports · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Deep learning-based label-free hematology analysis framework using optical diffraction tomography.Heliyon · 2023Article
- Article
- The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition.Nature communications · 2023Article
- Microscopic image-based classification of adipocyte differentiation by machine learning.Histochemistry and cell biology · 2023Article
- Review
- Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients.SLAS discovery : advancing life sciences R & D · 2021Article
- Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.Cells · 2021Article
- Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.Biomedical optics express · 2021Article
- Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning.BME frontiers · 2021Article
- Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.Nucleic acids research · 2020Article
- High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition.Scientific reports · 2020Article
- Quantitative assessment of morphology and sub-cellular changes in macrophages and trophoblasts during inflammation.Biomedical optics express · 2020Article
- Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing.Biomedical optics express · 2020Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.
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Registered trials
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