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CytoGAN: Generative Modeling of Cell Images

https://doi.org/10.1101/227645
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What this work says

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

inferredcites →https://doi.org/10.1016/j.copbio.2016.04.003openalex-snapshot
Applications in image-based profiling of perturbations
inferredcites →https://doi.org/10.1038/nmeth.2083openalex-snapshot
Annotated high-throughput microscopy image sets for validation
inferredcites →https://doi.org/10.1038/nmeth.4397openalex-snapshot
Data-analysis strategies for image-based cell profiling
inferredcites →https://doi.org/10.1093/bioinformatics/btw252openalex-snapshot
Classifying and segmenting microscopy images with deep multiple instance learning
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Automating Morphological Profiling with Generic Deep Convolutional Networks
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Improving Phenotypic Measurements in High-Content Imaging Screens
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Pipeline for illumination correction of images for high‐throughput microscopy
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/10.1177/1087057115623451openalex-snapshot
High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning
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/10.48550/arxiv.1501.01348openalex-snapshot
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data
inferredcites →https://doi.org/10.48550/arxiv.1511.06434openalex-snapshot
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.1705.00092openalex-snapshot
Generative Modeling with Conditional Autoencoders: Building an Integrated Cell

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cyto.a.23957openalex-snapshot
When Deep Learning Meets Cell Image Synthesis
inferred← citeshttps://doi.org/10.1007/978-1-0716-4011-1_3openalex-snapshot
Neuron Segmentation from Noisy Fluorescence Microscopy Images Using Deep Learning with Local and Global Scale Fusion
inferred← citeshttps://doi.org/10.1007/978-3-030-17297-8_17openalex-snapshot
Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis
inferred← citeshttps://doi.org/10.1007/978-3-030-30645-8_61openalex-snapshot
On Generative Modeling of Cell Shape Using 3D GANs
inferred← citeshttps://doi.org/10.1007/978-3-030-32778-1_9openalex-snapshot
Towards Annotation-Free Segmentation of Fluorescently Labeled Cell Membranes in Confocal Microscopy Images
inferred← citeshttps://doi.org/10.1007/978-3-030-87237-3_46openalex-snapshot
Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy
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/10.1007/978-3-031-08751-6_23openalex-snapshot
Weakly-Supervised Cell Classification for Effective High Content Screening
inferred← citeshttps://doi.org/10.1016/b978-0-12-824349-7.00014-1openalex-snapshot
Generative adversarial networks for medical image synthesis
inferred← citeshttps://doi.org/10.1016/b978-0-12-824349-7.00028-1openalex-snapshot
Review of cell image synthesis for image processing
inferred← citeshttps://doi.org/10.1016/j.bcp.2023.115770openalex-snapshot
High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106514openalex-snapshot
Novel Neural Style Transfer based data synthesis method for phase-contrast wound healing assay images
inferred← citeshttps://doi.org/10.1016/j.cbpa.2021.04.001openalex-snapshot
Image-based cell phenotyping with deep learning
inferred← citeshttps://doi.org/10.1016/j.cels.2018.06.001openalex-snapshot
Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
inferred← citeshttps://doi.org/10.1016/j.csbj.2024.02.022openalex-snapshot
Decoding phenotypic screening: A comparative analysis of image representations
inferred← citeshttps://doi.org/10.1016/j.matt.2023.04.009openalex-snapshot
Preventative studies should begin now for detecting AI-generated microscopy images
inferred← citeshttps://doi.org/10.1016/j.media.2022.102704openalex-snapshot
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
inferred← citeshttps://doi.org/10.1016/j.media.2023.102768openalex-snapshot
Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons
inferred← citeshttps://doi.org/10.1016/j.media.2023.102991openalex-snapshot
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
inferred← citeshttps://doi.org/10.1016/j.media.2025.103574openalex-snapshot
Generating realistic single-cell images from CellProfiler representations
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.09.007openalex-snapshot
High-content image generation for drug discovery using generative adversarial networks
inferred← citeshttps://doi.org/10.1016/j.patter.2021.100198openalex-snapshot
Latent representation learning in biology and translational medicine
inferred← citeshttps://doi.org/10.1016/j.tibtech.2021.03.006openalex-snapshot
Toward Deep Biophysical Cytometry: Prospects and Challenges
inferred← citeshttps://doi.org/10.1038/s41592-019-0458-zopenalex-snapshot
Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction
inferred← citeshttps://doi.org/10.1038/s41598-026-44976-6openalex-snapshot
A diffusion model conditioned on compound bioactivity profiles for generating high-content images
inferred← citeshttps://doi.org/10.1038/s42256-019-0017-4openalex-snapshot
Feedback GAN for DNA optimizes protein functions
inferred← citeshttps://doi.org/10.1038/s44320-026-00197-7openalex-snapshot
Progress and new challenges in image-based profiling.
inferred← citeshttps://doi.org/10.1042/ebc20180044openalex-snapshot
From observing to predicting single-cell structure and function with high-throughput/high-content microscopy
inferred← citeshttps://doi.org/10.1088/2632-2153/ad33e1openalex-snapshot
Generative adversarial networks for data-scarce radiative heat transfer applications
inferred← citeshttps://doi.org/10.1093/bib/bbae284openalex-snapshot
Morphological profiling for drug discovery in the era of deep learning
inferred← citeshttps://doi.org/10.1098/rsif.2017.0387openalex-snapshot
Opportunities and obstacles for deep learning in biology and medicine
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CardioVinci: building blocks for virtual cardiac cells using deep learning
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Using conditional Generative Adversarial Networks (GAN) to generate de novo synthetic cell nuclei for training machine learning-based image segmentation
inferred← citeshttps://doi.org/10.1101/2024.10.10.616543openalex-snapshot
A diffusion model conditioned on compound bioactivity profiles for predicting high-content images
inferred← citeshttps://doi.org/10.1101/238378openalex-snapshot
Building a 3D Integrated Cell
inferred← citeshttps://doi.org/10.1109/access.2023.3346273openalex-snapshot
A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing
inferred← citeshttps://doi.org/10.1109/bibm.2018.8621499openalex-snapshot
Heterogeneous Hi-C Data Super-resolution with a Conditional Generative Adversarial Network
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00970openalex-snapshot
Weakly Supervised Learning of Single-Cell Feature Embeddings
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00662openalex-snapshot
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis
inferred← citeshttps://doi.org/10.1109/cvprw59228.2023.00455openalex-snapshot
Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy
inferred← citeshttps://doi.org/10.1109/iccvw60793.2023.00429openalex-snapshot
Virtual perturbations to assess explainability of deep-learning based cell fate predictors
inferred← citeshttps://doi.org/10.1109/isbi48211.2021.9433896openalex-snapshot
Cellcyclegan: Spatiotemporal Microscopy Image Synthesis Of Cell Populations Using Statistical Shape Models And Conditional Gans
inferred← citeshttps://doi.org/10.1109/isbi61048.2026.11515611openalex-snapshot
High-Quality Synthetic Annotated Tissue Data Using Conditional Generative Adversarial Networks
inferred← citeshttps://doi.org/10.1109/tmi.2020.3031029openalex-snapshot
A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes
inferred← citeshttps://doi.org/10.1109/tmi.2022.3191011openalex-snapshot
Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction
inferred← citeshttps://doi.org/10.1109/tocs50858.2020.9339748openalex-snapshot
An overview of biological data generation using generative adversarial networks
inferred← citeshttps://doi.org/10.1117/12.2512660openalex-snapshot
Variational autoencoding tissue response to microenvironment perturbation
inferred← citeshttps://doi.org/10.1128/spectrum.01472-22openalex-snapshot
A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies
inferred← citeshttps://doi.org/10.1145/3794209.3794219openalex-snapshot
Diffuse, Synthesize, Classify: Generating Synthetic Leukocyte Images to Train Privacy-Preserving Classifier
inferred← citeshttps://doi.org/10.1186/s12859-022-04845-1openalex-snapshot
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009155openalex-snapshot
A deep generative model of 3D single-cell organization
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1009888openalex-snapshot
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic
inferred← citeshttps://doi.org/10.1515/cdbme-2022-1187openalex-snapshot
Patch Based Classification of Cell Painted ER and Cytoplasm using Block Intensity Gradient Pattern and Multilayer Perceptron
inferred← citeshttps://doi.org/10.15252/msb.20199198openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/shti220626openalex-snapshot
Differentiation of Cell Painted Organelles Using Non Local Texture Descriptor and Random Forest Approach
inferred← citeshttps://doi.org/10.3389/fbioe.2024.1500270openalex-snapshot
Deep learning methods for high-resolution microscale light field image reconstruction: a survey
inferred← citeshttps://doi.org/10.3389/fcomp.2019.00010openalex-snapshot
Generative Adversarial Networks for Augmenting Training Data of Microscopic Cell Images
inferred← citeshttps://doi.org/10.3389/fmicb.2025.1536131openalex-snapshot
Advancing antibiotic discovery with bacterial cytological profiling: a high-throughput solution to antimicrobial resistance
inferred← citeshttps://doi.org/10.3390/s22010206openalex-snapshot
Generative Adversarial Networks for Morphological–Temporal Classification of Stem Cell Images
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
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