ArticleCell reports methods2021
A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.
Article in Cell reports methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- An Integrated Micromirror System for High-Throughput Multi-Parametric Cell Imaging.Small methods · 2026Article
- Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential.Microsystems & nanoengineering · 2026Article
- Review
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides.Nature communications · 2024Article
- Label-free macrophage phenotype classification using machine learning methods.Scientific reports · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Artificial intelligence in imaging flow cytometry.Frontiers in bioinformatics · 2023Article
- Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data.iScience · 2022Article
- Practical machine learning for disease diagnosis.Cell reports methods · 2021Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.
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