mlsanityData to 14 Oct 2026

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification

arXiv 2508.04457 ↗First posted 6 Aug 2025stat.ML cs.LGcited by 4

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Abstract (arXiv)

Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored. In this study, we provide an extensive uncertainty quantification benchmark for multi-label chest X-ray classification using the MIMIC-CXR-JPG dataset. We evaluate 13 uncertainty quantification methods for convolutional (ResNet) and transformer-based (Vision Transformer) architectures across a wide range of tasks. Additionally, we extend Evidential Deep Learning, HetClass NNs, and Deep Deterministic Uncertainty to the multi-label setting. Our analysis provides insights into uncertainty estimation effectiveness and the ability to disentangle epistemic and aleatoric uncertainties, revealing method- and architecture-specific strengths and limitations.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 6 Aug 2025.

Builds on

22 of 33 references.

References · 22

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  4. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
  5. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  6. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  7. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  8. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
  9. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  10. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  11. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
  12. 2019Learning Loss for Active Learningcited by 336
10 more
  1. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 231
  2. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  3. 2015Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networkscited by 139
  4. 2024Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Taskscited by 78
  5. 2021Correlated Input-Dependent Label Noise in Large-Scale Image Classificationcited by 37
  6. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  7. 2021Transformer Uncertainty Estimation with Hierarchical Stochastic Attentioncited by 21
  8. 2023Massively Scaling Heteroscedastic Classifierscited by 11
  9. 2022From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathologycited by 9
  10. 2024EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methodscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentationcited by 1
  2. 2025Enhancing Multi-Label Thoracic Disease Diagnosis with Deep Ensemble-Based Uncertainty Quantificationcited by 0
  3. 2026Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributionscited by 0
  4. 2026Structural Limits of the Information-Theoretic Uncertainty Decompositioncited by 0