ArticleBiomedical optics express2020
Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells.Biosensors · 2026Article
- Frequency-Guided Cross-Modal Interaction for Multimodal Yeast Classification Based on Light-Scattering and Microscopy Images.Journal of imaging · 2026Article
- Advanced automated classification and segmentation of leukemic cells using simulated optical scanning holography and active contour methods.Journal of biomedical optics · 2025Article
- Electrochemical-assisted scattering imaging system for lymphoma cell classification using machine learning.Biomedical optics express · 2025Article
- Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis.Biomedical optics express · 2024Article
- Imaging the intracellular refractive index distribution (IRID) for dynamic label-free living colon cancer cells via circularly depolarization decay model (CDDM).Biomedical optics express · 2024Article
- Unknown cell class distinction via neural network based scattering snapshot recognition.Biomedical optics express · 2023Article
- Three-dimensional deep regression-based light scattering imaging system for nanoscale exosome analysis.Biomedical optics express · 2023Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.
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Registered trials
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