Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
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Abstract (arXiv)
Label-free chemical imaging holds significant promise for improving digital pathology workflows, but data acquisition speed remains a limiting factor. To address this gap, we propose an adaptive strategy-initially scan the low information (LI) content of the entire tissue quickly, identify regions with high aleatoric uncertainty (AU), and selectively re-image them at better quality to capture higher information (HI) details. The primary challenge lies in distinguishing between high-AU regions mitigable through HI imaging and those that are not. However, since existing uncertainty frameworks cannot separate such AU subcategories, we propose a fine-grained disentanglement method based on post-hoc latent space analysis to unmix resolvable from irresolvable high-AU regions. We apply our approach to streamline infrared spectroscopic imaging of breast tissues, achieving superior downstream segmentation performance. This marks the first study focused on fine-grained AU disentanglement within dynamic image spaces (LI-to-HI), with novel application to streamline histopathology.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 38 of its 53 references that are in the map. First posted on arXiv 27 Feb 2025.
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38 of 53 references.
References · 38
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
- 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
- 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
- 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
- 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
- 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
- 2019Verified Uncertainty Calibrationcited by 307
26 more
- 2019Cyclical Stochastic Gradient MCMC for Bayesian Deep Learningcited by 203
- 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
- 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
- 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 163
- 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
- 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
- 2020Parametric UMAP embeddings for representation and semi-supervised learningcited by 103
- 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 102
- 2022A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglementcited by 87
- 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Taskscited by 78
- 2021Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detectioncited by 67
- 2022Full-Spectrum Out-of-Distribution Detectioncited by 63
- 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
- 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
- 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
- 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
- 2020Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detectioncited by 26
- 2022Uncertainty Estimates of Predictions via a General Bias-Variance Decompositioncited by 20
- 2023Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Datacited by 20
- 2023How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?cited by 15
- 2022Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbedcited by 12
- 2023Massively Scaling Heteroscedastic Classifierscited by 11
- 2024Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentationcited by 9
- 2022Task Agnostic and Post-hoc Unseen Distribution Detectioncited by 8
- 2024Are We Ready for Out-of-Distribution Detection in Digital Pathology?cited by 8
- 2022What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorizationcited by 5
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