ArticleSensors (Basel, Switzerland)2021
Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Cell-Based Immuno-Biosensors Using Microfluidics.Sensors (Basel, Switzerland) · 2026Review
- 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
- An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder.Life (Basel, Switzerland) · 2023Article
- UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells.Sensors (Basel, Switzerland) · 2023Article
- Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations.Scientific reports · 2022Article
- Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.Sensors (Basel, Switzerland) · 2021Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet.Entropy (Basel, Switzerland) · 2021Article
- High-Efficiency Classification of White Blood Cells Based on Object Detection.Journal of healthcare engineering · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.
Indexed as
Identifiers
What OpenQuestion holds
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.