ArticleScientific reports2016
Deep Learning in Label-free Cell Classification.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
154 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis.Frontiers in oncology · 2022Pooled it
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Three-dimensional forward-scattering imaging flow cytometry system for single-cell analysis.APL photonics · 2025Article
- Deep Learning-Based Prediction of Individual CellBiosensors · 2025Article
- Enhancing cell characterization with microfluidics and AI: a comprehensive review of mechanical, electrical, and hybrid techniques.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- Advancing cell therapies with artificial intelligence and synthetic biology.Current opinion in biomedical engineering · 2025Article
- MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems.Microsystems & nanoengineering · 2025Article
- Article
- Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second.Light, science & applications · 2025Article
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- Generative adversarial network model to classify human induced pluripotent stem cell-cardiomyocytes based on maturation level.Scientific reports · 2024Article
- Manufacturing, Processing, and Characterization of Self-Expanding Metallic Stents: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation.Foods (Basel, Switzerland) · 2024Article
- Data-driven classification of individual cells by their non-Markovian motion.Biophysical journal · 2024Article
- Review
- Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients.Clinical and experimental medicine · 2024Review
- Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.Stem cell research & therapy · 2024Article
- Patient-Derived Microphysiological Systems for Precision Medicine.Advanced healthcare materials · 2024Review
- Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit.Communications biology · 2024Article
94 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
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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.