ReviewJMIR medical informatics2022
Uncertainty Estimation in Medical Image Classification: Systematic Review.
Review in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Protect the Brain When Treating the Heart: Feasibility of 2.5D U-Net for Real-Time Gaseous Microemboli Detection.Bioengineering (Basel, Switzerland) · 2026Article
- A diffusion-conditioned representation learning framework for disease classification in medical imaging.BMC research notes · 2026Article
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier From Lumbar Sagittal T2-Weighted MRI.JOR spine · 2026Article
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- KDH-Net: Explainable Medical AI for Multiclass Kidney Disease Characterization from CT Images.Journal of clinical medicine · 2026Article
- Patient-specific digital twins in aortic disease: integrating computational hemodynamics, immune profiling, and precision endovascular strategy.Frontiers in cardiovascular medicine · 2026Review
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
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- YOLO-TARC: YOLOv10 with Token Attention and Residual Convolution for Small Void Detection in Root Canal X-Ray Images.Sensors (Basel, Switzerland) · 2025Article
- Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.Journal of imaging informatics in medicine · 2025Article
- CoronaryDominance: Angiogram dataset for coronary dominance classification.Scientific data · 2025Article
- Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024Article
- Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.European radiology · 2024Article
- A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.medRxiv : the preprint server for health sciences · 2024Article
- Quantitative evaluation model of variable diagnosis for chest X-ray images using deep learning.PLOS digital health · 2024Article
- The path toward equal performance in medical machine learning.Patterns (New York, N.Y.) · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network's uncertainty together with its prediction.
objectiveIn this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation.
methodsGoogle Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms "uncertainty," "uncertainty estimation," "network calibration," and "out-of-distribution detection" were used in combination with the terms "medical images," "medical image analysis," and "medical image classification."
resultsA total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty.
conclusionsThe applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11936.
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