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Automating Morphological Profiling with Generic Deep Convolutional Networks

https://doi.org/10.1101/085118
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What this work says

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

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ImageNet Large Scale Visual Recognition Challenge
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Applications in image-based profiling of perturbations
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Deep learning
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Annotated high-throughput microscopy image sets for validation
inferredcites →https://doi.org/10.1093/bioinformatics/btw252openalex-snapshot
Classifying and segmenting microscopy images with deep multiple instance learning
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1111/jmi.12178openalex-snapshot
Pipeline for illumination correction of images for high‐throughput microscopy
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/10.1177/1087057115623451openalex-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/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.3109/10409238.2015.1135868openalex-snapshot
Computer vision for high content screening
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1501.01348openalex-snapshot
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data

What points at this work

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

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Predicting cell morphological responses to perturbations using generative modeling
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Self-supervision advances morphological profiling by unlocking powerful image representations
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Genetic analysis and multimodal imaging identify novel mtDNA 12148T>C leading to multisystem dysfunction with tissue-specific heteroplasmy
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Deep Learning-based Image Analysis for High-content Screening
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