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Unsupervised phenotypic analysis of cellular images with multi-scale convolutional neural networks

https://doi.org/10.1101/361410
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cyto.a.20662openalex-snapshot
Single‐cell‐based image analysis of high‐throughput cell array screens for quantification of viral infection
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1016/j.cell.2015.11.007openalex-snapshot
Microscopy-Based High-Content Screening
inferredcites →https://doi.org/10.1016/j.jneumeth.2010.07.003openalex-snapshot
An imaging assay to analyze primary neurons for cellular neurotoxicity
inferredcites →https://doi.org/10.1016/j.neuron.2015.02.023openalex-snapshot
Cell-Based Screening: Extracting Meaning from Complex Data
inferredcites →https://doi.org/10.1038/msb.2010.25openalex-snapshot
Clustering phenotype populations by genome‐wide RNAi and multiparametric imaging
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot
Dermatologist-level classification of skin cancer with deep neural networks
inferredcites →https://doi.org/10.1038/nmeth.2083openalex-snapshot
Annotated high-throughput microscopy image sets for validation
inferredcites →https://doi.org/10.1038/nmeth.2476openalex-snapshot
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction
inferredcites →https://doi.org/10.1038/nmeth.4397openalex-snapshot
Data-analysis strategies for image-based cell profiling
inferredcites →https://doi.org/10.1038/nmeth1032openalex-snapshot
Image-based multivariate profiling of drug responses from single cells
inferredcites →https://doi.org/10.1038/nrg3768openalex-snapshot
Single-cell and multivariate approaches in genetic perturbation screens
inferredcites →https://doi.org/10.1091/mbc.e17-05-0333openalex-snapshot
A deep learning and novelty detection framework for rapid phenotyping in high-content screening
inferredcites →https://doi.org/10.1091/mbc.e17-09-0551openalex-snapshot
Quantitative cell biology: uniting disciplines to understand the cell
inferredcites →https://doi.org/10.1093/bioinformatics/btl140openalex-snapshot
Improved scoring of functional groups from gene expression data by decorrelating GO graph structure
inferredcites →https://doi.org/10.1093/bioinformatics/btx069openalex-snapshot
A multi-scale convolutional neural network for phenotyping high-content cellular images
inferredcites →https://doi.org/10.1101/085118openalex-snapshot
Automating Morphological Profiling with Generic Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot
Multi-column deep neural networks for image classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/icip.2007.4379564openalex-snapshot
Multiscale Variance-Stabilizing Transform for Mixed-Poisson-Gaussian Processes and its Applications in Bioimaging
inferredcites →https://doi.org/10.1109/tip.2005.859378openalex-snapshot
Image information and visual quality
inferredcites →https://doi.org/10.1109/tpami.2015.2496141openalex-snapshot
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1126/science.1140324openalex-snapshot
Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1158/1535-7163.mct-09-1148openalex-snapshot
High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells
inferredcites →https://doi.org/10.1177/1087057113503553openalex-snapshot
Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment
inferredcites →https://doi.org/10.1177/1087057116631284openalex-snapshot
Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1186/1471-2105-14-336openalex-snapshot
A novel phenotypic dissimilarity method for image-based high-throughput screens
inferredcites →https://doi.org/10.1186/gb-2006-7-10-r100openalex-snapshot
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
inferredcites →https://doi.org/10.1242/jcs.123604openalex-snapshot
Machine learning in cell biology – teaching computers to recognize phenotypes
inferredcites →https://doi.org/10.1371/journal.pone.0050514openalex-snapshot
Automated Analysis and Reannotation of Subcellular Locations in Confocal Images from the Human Protein Atlas
inferredcites →https://doi.org/10.15252/msb.20156651openalex-snapshot
Deep learning for computational biology
inferredcites →https://doi.org/10.15252/msb.20177551openalex-snapshot
Automated analysis of high‐content microscopy data with deep learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-34356-9_35openalex-snapshot
Training Multiscale-CNN for Large Microscopy Image Classification in One Hour
inferred← citeshttps://doi.org/10.1007/978-3-030-87589-3_58openalex-snapshot
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification
inferred← citeshttps://doi.org/10.1007/978-3-032-09513-8_42openalex-snapshot
Scheduled Cross-Domain Multi-center DINO for Robust High-Content Screening Representation Learning
inferred← citeshttps://doi.org/10.1016/j.slasd.2023.08.009openalex-snapshot
Evolution and impact of high content imaging
inferred← citeshttps://doi.org/10.1021/acschembio.9b00141openalex-snapshot
Morphological Deconvolution of Beta-Lactam Polyspecificity in E. coli
inferred← citeshttps://doi.org/10.1021/acsinfecdis.1c00312openalex-snapshot
Morphological Characterization of Antibiotic Combinations
inferred← citeshttps://doi.org/10.1038/s41378-023-00562-8openalex-snapshot
Computer vision meets microfluidics: a label-free method for high-throughput cell analysis
inferred← citeshttps://doi.org/10.1093/bib/bbae284openalex-snapshot
Morphological profiling for drug discovery in the era of deep learning
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab497openalex-snapshot
Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
inferred← citeshttps://doi.org/10.1101/2022.05.15.491989openalex-snapshot
High-content cellular screen image analysis benchmark study
inferred← citeshttps://doi.org/10.1101/2022.09.11.507500openalex-snapshot
In silico labeling enables kinetic myelination assay in brightfield
inferred← citeshttps://doi.org/10.1101/2023.05.01.538999openalex-snapshot
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
inferred← citeshttps://doi.org/10.1101/725754openalex-snapshot
Metadata-Guided Visual Representation Learning for Biomedical Images
inferred← citeshttps://doi.org/10.1101/824862openalex-snapshot
Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis
inferred← citeshttps://doi.org/10.1109/tmi.2019.2905841openalex-snapshot
Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks
inferred← citeshttps://doi.org/10.34133/2022/9861263openalex-snapshot
Deep Learning in Cell Image Analysis
inferred← citeshttps://doi.org/10.48550/arxiv.2008.11673openalex-snapshot
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
inferred← citeshttps://doi.org/10.48550/arxiv.2012.07421openalex-snapshot
WILDS: A Benchmark of in-the-Wild Distribution Shifts
inferred← citeshttps://doi.org/10.70675/6cabf9efza834z492cz9634z82db6ebe2b3fopenalex-snapshot
Etude des méthodes d'apprentissage profond pour la classification et la segmentation des chromosome et des images pulmonaires
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/2openalex-snapshot
Peer Review #2 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.1/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/1openalex-snapshot
Peer Review #1 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj.8594v0.2/reviews/3openalex-snapshot
Peer Review #3 of "Correcting nuisance variation using Wasserstein distance (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj.8594openalex-snapshot
Correcting nuisance variation using Wasserstein distance
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.