ArticlePloS one2019
Convolutional neural network for cell classification using microscope images of intracellular actin networks.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 73 citations in OpenAlex.
- Advances in fluorescence-based point-of-care diagnostics: probes, nanostructures and integrated systems.Journal of materials chemistry. C · 2026Review
- Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets.IEEE open journal of engineering in medicine and biology · 2026Article
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- A dual-branch deep learning network for circulating tumor cells classification.Journal of translational medicine · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Optimizing CAR-T cell therapy for solid tumors: current challenges and potential strategies.Journal of hematology & oncology · 2024Review
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.Journal of biomedical optics · 2024Article
- Distinct platelet F-actin patterns and traction forces on von Willebrand factor versus fibrinogen.Biophysical journal · 2023Article
- Temporal focusing multiphoton microscopy with cross-modality multi-stage 3D U-Net for fast and clear bioimaging.Biomedical optics express · 2023Article
- Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs.Sensors (Basel, Switzerland) · 2023Article
- Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury.Scientific reports · 2023Article
- Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape.Sensors (Basel, Switzerland) · 2023Article
- Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.PloS one · 2023Article
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants.Bioinformatics (Oxford, England) · 2022Article
- AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network.Journal of molecular cell biology · 2022Article
- An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images.Scientific reports · 2022Article
- Article
- Convolutional Blur Attention Network for Cell Nuclei Segmentation.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using label-free cellular images obtained from an optical microscope. Although these studies showed promising results, such classifiers were not able to reflect the biological diversity of different types of cell. While in terms of malignant cell, it is well-known that intracellular actin filaments are altered substantially. This is thought to be closely related to the abnormal growth features of tumor cells, their ability to invade surrounding tissues and also to metastasize. Therefore, being able to classify different types of cell based on their biological behaviors using automated technique is more advantageous. This article reveals the difference in the actin cytoskeleton structures between breast normal and cancer cells, which may provide new information regarding malignant changes and be used as additional diagnostic marker. Since the features cannot be well detected by human eyes, we proposed the application of convolutional neural network (CNN) in cell classification based on actin-labeled fluorescence microscopy images. The CNN was evaluated on a large number of actin-labeled fluorescence microscopy images of one human normal breast epithelial cell line and two types of human breast cancer cell line with different levels of aggressiveness. The study revealed that the CNN performed better in the cell classification task compared to a human expert.
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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.