The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Label‐free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis
Open accessAbstract
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time‐consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label‐free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO‐v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label‐free method for cellular analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“| YOLO v3 [72] | Accuracy, Specificity, Sensitivity | Introduced a label-free method for cellular analysis using 2D light scattering and neural networks. | Oncology |”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
Survival and Prognostic Stratification of 670 Patients With Advanced Renal Cell Carcinoma
Motzer, R. J., Mazumdar, M., Bacik, J., Berg, W. J., Amsterdam, A., & Ferrara, J. M. (1999) · Journal of Clinical Oncology · doi:10.1200/jco.1999.17.8.2530 · Cited by 1,797
Principles and Methods of Toxicology
(1984) · Food and Chemical Toxicology · doi:10.1016/0278-6915(84)90066-8 · Cited by 1,427
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Flow cytometric immunophenotyping for hematologic neoplasms
Craig, F. E., & Foon, K. A. (2008) · Blood · doi:10.1182/blood-2007-11-120535 · Cited by 674
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Ferrari, V., Marín-Jiménez, M. J., & Zisserman, A. (2008) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 397
Lab on a chip for continuous-flow magnetic cell separation
Hejazian, M., Li, W., & Nguyen, N.-T. (2015) · Lab on a Chip · doi:10.1039/c4lc01422g · Cited by 347 · cited 1×
“It is often used to monitor cell proliferation and health status for cultured cells; to in vitro evaluate drug or environmental cytotoxicity effects on target cells; to test the cell viabilities after a series of operations, such as cell separating [4], sorting [5], and capturing [6].”
Kamande, J. W., Hupert, M. L., Witek, M. A., Wang, H., Torphy, R. J., Dharmasiri, U., Njoroge, S. K., Jackson, J. M., Aufforth, R., Snavely, A. C., et al. (2013) · Analytical Chemistry · doi:10.1021/ac401720k · Cited by 112
Improving efficiency of viability-PCR for selective detection of live cells
Nkuipou‐Kenfack, E., Engel, H., Fakih, S., & Nocker, A. (2013) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2013.01.018 · Cited by 101
Research works printed but not linked: 1
- Shin YJ, 2020, Cell image detection in cervical cancer cells using YoloV3, Basic Clin Pharmacol, 127, 27
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)
Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., & Alhussian, H. (2024) · IEEE Access · doi:10.1109/access.2024.3386826 · Cited by 306
Computer vision meets microfluidics: a label-free method for high-throughput cell analysis
Zhou, S., Chen, B., Fu, E. S., & Yan, H. G. (2023) · Microsystems & Nanoengineering · doi:10.1038/s41378-023-00562-8 · Cited by 79
Self-citationA Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)Preprint
Qureshi, R., Ragab, M. G., Abdulkadir, S. J., Muneer, A., ALQUSHAIB, A., Sumiea, E. H., & Alhussian, H. (2023) · doi:10.36227/techrxiv.23681679 · Cited by 31
Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation
Lan, Y., Huang, N., Fu, Y., Liu, K., Zhang, H., Li, Y., & Yang, S. L. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.802794 · Cited by 28
Self-citation
Metadata
- Authors
- Li, S., Li, Y., Yao, J., Chen, B., Song, J., Xue, Q., & Yang, X.
- Affiliations
- Zhengzhou University (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
- Corresponding author
- Xue, Qi
- Yang, Xiaonan
- Venue
- Cytometry Part A
- Publisher
- Wiley
- Published
- 2021-06-19
- Volume
- 99
- Issue
- 11
- Pages
- 1134-1142
- ISSN
- 1552-4922 (ISSN-L)
- 1552-4930
- OpenAlex
- W3170879873
- Cited by
- 13
- References
- 24
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 2
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cytometry Part A (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61874099, 81802325)
- China Postdoctoral Science Foundation (2018M642796)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Digital Holography and Microscopy
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- HT-29 cells, YOLOv3, deep convolutional neural networks
- MeSH terms
- Deep Learning, Adenocarcinoma, Algorithms, Humans, Staining and Labeling, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
