mlsanityData to 14 Oct 2026

Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation

arXiv 2208.06038 ↗First posted 11 Aug 2022eess.IV cs.CV cs.LGcited by 21

Not on the map. Why?

Abstract (arXiv)

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and robust segmentation results. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 11 Aug 2022.

Builds on

23 of 42 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  7. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  8. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  9. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  10. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  11. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  12. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 283
11 more
  1. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  2. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  3. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  4. 2019Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustnesscited by 104
  5. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  6. 2017Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networkscited by 42
  7. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  8. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
  9. 2021Evidential fully convolutional network for semantic segmentationcited by 11
  10. 2022Large-Kernel Attention for 3D Medical Image Segmentationcited by 7
  11. 2019Robustness of Brain Tumor Segmentationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 6; 2025: 3; 2026: 6.

21 papers.

Sort:

By area:

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 63
  2. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  3. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 28
  4. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  5. 2023Evidential Uncertainty Quantification: A Variance-Based Perspectivecited by 14
  6. 2023Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximizationcited by 7
  7. 2024An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentationcited by 5
  8. 2023Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentationcited by 1
  9. 2024DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentationcited by 1
  10. 2024DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRIcited by 0
  11. 2024Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentationcited by 0
  12. 2024Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantificationcited by 0
9 more
  1. 2025UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomographycited by 0
  2. 2025Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networkscited by 0
  3. 2025Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentationcited by 0
  4. 2026Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classificationcited by 0
  5. 2026U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable explorationcited by 0
  6. 2026Uncertainty-Guided Conservative Propagation for Structured Inference in Vessel Segmentationcited by 0
  7. 2026Variational Inference for Evidential Deep Learningcited by 0
  8. 2026Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decouplingcited by 0
  9. 2026Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognitioncited by 0