ArticlePLoS computational biology2019
Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- Morphology-robust quantification of subcellular organization in complex cells.bioRxiv : the preprint server for biology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- Article
- Prediction of protein subcellular localization in single cells.Nature methods · 2025Article
- Self-supervision advances morphological profiling by unlocking powerful image representations.Scientific reports · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- Decoding phenotypic screening: A comparative analysis of image representations.Computational and structural biotechnology journal · 2024Article
- Self-supervised learning framework application for medical image analysis: a review and summary.Biomedical engineering online · 2024Review
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.Journal of pharmaceutical sciences · 2024Article
- Morphological profiling for drug discovery in the era of deep learning.Briefings in bioinformatics · 2024Review
- PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.Molecular systems biology · 2024Article
- Learning representations for image-based profiling of perturbations.Nature communications · 2024Article
- Microsnoop: A generalist tool for microscopy image representation.Innovation (Cambridge (Mass.)) · 2024Article
- Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.Neurophotonics · 2023Article
- An introduction to representation learning for single-cell data analysis.Cell reports methods · 2023Review
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4 authors.
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Abstract
Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.
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