Uncertainty Evaluation Metric for Brain Tumour Segmentation
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Abstract (arXiv)
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2020.
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Citing papers per year: 2021: 7; 2022: 1; 2023: 0; 2024: 3.
11 papers.
- 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
- 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
- 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
- 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
- 2021Robustness via Uncertainty-aware Cycle Consistencycited by 16
- 2024QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challengecited by 12
- 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregationcited by 12
- 2021A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentationcited by 9
- 2021HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Imagescited by 5
- 2021SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networkscited by 4
- 2021Uncertainty-aware Generalized Adaptive CycleGANcited by 3
- 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation5 Jun 2024
- 2024QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge19 Mar 2024
- 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation16 Jan 2024
- 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis5 Oct 2022
- 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results19 Dec 2021
- 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec 2021
- 2021SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks10 Nov 2021
- 2021Robustness via Uncertainty-aware Cycle Consistency24 Oct 2021
- 2021A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation22 Sep 2021
- 2021HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images30 Mar 2021
- 2021Uncertainty-aware Generalized Adaptive CycleGAN23 Feb 2021