ArticleIEEE transactions on medical imaging2020
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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83 citing papers in PubMed.
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- Automated framework for volumetric musculoskeletal segmentation and assessment of torso CT images in large-scale databases.International journal of computer assisted radiology and surgery · 2026Article
- A unified medical image segmentation evaluation method combining multi-metrics and confidence.Medical & biological engineering & computing · 2026Article
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- ChemoCardioNet: An Explainable Multimodal Transformer for Early Prediction of Chemotherapy-Induced Cardiotoxicity.Journal of cardiovascular translational research · 2026Article
- Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments.International journal of computer assisted radiology and surgery · 2026Article
- Mind the Gap: Human and AI Uncertainties in Cardiac MRI Segmentation.Cardiovascular engineering and technology · 2026Article
- Uncertainty-guided model learning for trustworthy medical image segmentation.Medical & biological engineering & computing · 2026Article
- Improving confidence in MRI-based auto-segmentation via uncertainty assessment.Acta oncologica (Stockholm, Sweden) · 2026Article
- Trusted polyp segmentation via multi-scale evidence ensemble in colonoscopy images.International journal of computer assisted radiology and surgery · 2026Article
- Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.Computer methods and programs in biomedicine · 2026Article
- TrustNet: a lightweight network with integrated uncertainty quantification and quantitative explainable AI for ischemic stroke detection in CT images.Scientific reports · 2026Article
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
- DARE-FUSE: domain aligned evidence guided learning for joint brain tumor MRI segmentation and classification.NPJ digital medicine · 2026Article
- Random forest-based out-of-distribution detection for robust lung cancer segmentation.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
23 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration.
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