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Bayesian Neural Networks for Cellular Image Classification and Uncertainty Analysis

https://doi.org/10.1101/824862
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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.strusafe.2008.06.020openalex-snapshot
Aleatory or epistemic? Does it matter?
inferredcites →https://doi.org/10.1023/a:1007665907178openalex-snapshot
An Introduction to Variational Methods for Graphical Models
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/nature14541openalex-snapshot
Probabilistic machine learning and artificial intelligence
inferredcites →https://doi.org/10.1038/nmeth.2083openalex-snapshot
Annotated high-throughput microscopy image sets for validation
inferredcites →https://doi.org/10.1038/s41598-017-17876-zopenalex-snapshot
Leveraging uncertainty information from deep neural networks for disease detection
inferredcites →https://doi.org/10.1080/01621459.1988.10478694openalex-snapshot
Bayesian Variable Selection in Linear Regression
inferredcites →https://doi.org/10.1089/adt.2018.859openalex-snapshot
Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes
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/10.1101/161422openalex-snapshot
Improving Phenotypic Measurements in High-Content Imaging Screens
inferredcites →https://doi.org/10.1101/361410openalex-snapshot
Unsupervised phenotypic analysis of cellular images with multi-scale convolutional neural networks
inferredcites →https://doi.org/10.1109/72.623216openalex-snapshot
Are artificial neural networks black boxes?
inferredcites →https://doi.org/10.1109/ijcnn.2003.1223669openalex-snapshot
Coloring black boxes: visualization of neural network decisions
inferredcites →https://doi.org/10.1145/168304.168306openalex-snapshot
Keeping the neural networks simple by minimizing the description length of the weights
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/10.1177/1087057115623451openalex-snapshot
High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning
inferredcites →https://doi.org/10.1201/b10905-10openalex-snapshot
Spatial Point Processes
inferredcites →https://doi.org/10.17863/cam.11691openalex-snapshot
Approximate inference in graphical models
inferredcites →https://doi.org/10.17863/cam.28034openalex-snapshot
Variational Bayesian dropout: pitfalls and fixes
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1207.0580openalex-snapshot
Improving neural networks by preventing co-adaptation of feature detectors
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot
Auto-Encoding Variational Bayes
inferredcites →https://doi.org/10.48550/arxiv.1505.05424openalex-snapshot
Weight Uncertainty in Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1506.02142openalex-snapshot
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1506.02557openalex-snapshot
Variational Dropout and the Local Reparameterization Trick
inferredcites →https://doi.org/10.48550/arxiv.1602.02666openalex-snapshot
A Variational Analysis of Stochastic Gradient Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1702.05373openalex-snapshot
EMNIST: an extension of MNIST to handwritten letters
inferredcites →https://doi.org/10.48550/arxiv.1703.04977openalex-snapshot
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
inferredcites →https://doi.org/10.48550/arxiv.1704.02685openalex-snapshot
Learning Important Features Through Propagating Activation Differences
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.5555/2567709.2502622openalex-snapshot
Stochastic variational inference
inferredcites →https://doi.org/10.5555/3122009.3208015openalex-snapshot
Stochastic Gradient Descent as Approximate Bayesian Inference

What points at this work

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

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Uncertainty-Based Instance-Dependent Noisy Label Datasets Generation
inferred← citeshttps://doi.org/10.1007/s10009-021-00623-1openalex-snapshot
Neural predictive monitoring and a comparison of frequentist and Bayesian approaches
inferred← citeshttps://doi.org/10.1016/j.rse.2021.112408openalex-snapshot
Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
inferred← citeshttps://doi.org/10.1038/s41746-025-01580-8openalex-snapshot
Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology
inferred← citeshttps://doi.org/10.1080/00207721.2025.2490618openalex-snapshot
Scalable and reliable stochastic parametric verification with stochastic variational smoothed model checking
inferred← citeshttps://doi.org/10.1101/2024.11.01.621561openalex-snapshot
Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology
inferred← citeshttps://doi.org/10.1103/physrevd.103.063034openalex-snapshot
Detection of gravitational waves using Bayesian neural networks
inferred← citeshttps://doi.org/10.1109/edis57230.2022.9996478openalex-snapshot
Bayesian Convolutional Neural Networks for Image Classification with Uncertainty Estimation
inferred← citeshttps://doi.org/10.11606/d.3.2022.tde-10072024-142738openalex-snapshot
Redes neurais bayesianas para calibração de sensores de poluição do ar e detecção de falhas em processos químicos.
inferred← citeshttps://doi.org/10.7557/18.6802openalex-snapshot
Hybrid bayesian convolutional neural network object detection architectures for tracking small markers in automotive crashtest videos
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