ArticleSLAS discovery : advancing life sciences R & D2019
Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes.
Article in SLAS discovery : advancing life sciences R & D, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- ExoFILT: transfer learning for robust and accelerated analysis of exocytosis single-particle tracking data.Bioinformatics (Oxford, England) · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- HUCSR-Net: Automated Pulmonary Embolism Detection Using 3D Deep Learning.F1000Research · 2026Article
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.Biology methods & protocols · 2025Article
- HoloMoA: a holography and deep learning tool for the identification of antimicrobial mechanisms of action and the detection of novel MoA.Frontiers in microbiology · 2025Article
- uniDINO: Assay-independent feature extraction for fluorescence microscopy images.Computational and structural biotechnology journal · 2025Article
- Decoding phenotypic screening: A comparative analysis of image representations.Computational and structural biotechnology journal · 2024Article
- CPSign: conformal prediction for cheminformatics modeling.Journal of cheminformatics · 2024Article
- SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature Learning.Sensors (Basel, Switzerland) · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
- Learning representations for image-based profiling of perturbations.Nature communications · 2024Article
- Transfer learning for versatile and training free high content screening analyses.Scientific reports · 2023Article
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Evaluating the utility of brightfield image data for mechanism of action prediction.PLoS computational biology · 2023Article
- Automated Sagittal Skeletal Classification of Children Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several nontrivial and independent analysis steps. Recently, convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study, we applied the pretrained deep convolutional neural networks ResNet50, InceptionV3, and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pretrained on ImageNet, enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95% and 97%. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labeled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.
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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.