ArticleNeural computing & applications2023
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation.
Article in Neural computing & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- The Physical Fidelity Gap as an Evidence-Traceability Problem in AI Uncertainty Quantification: A Structured Review.Sensors (Basel, Switzerland) · 2026Review
- Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know.Abdominal radiology (New York) · 2026Review
- Uncertainty Quantification in Medical Image Segmentation: A Comprehensive Survey.Journal of imaging · 2026Review
- Segmenting with confidence through uncertainty quantification for brain tumor imaging.NPJ digital medicine · 2026Article
- A lightweight crack segmentation network based on the importance-enhanced Mamba model.Scientific reports · 2025Article
- BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.Neural networks : the official journal of the International Neural Network Society · 2025Article
- DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.Translational cancer research · 2024Article
- Large-Kernel Attention for 3D Medical Image Segmentation.Cognitive computation · 2024Article
- M-MSSEU: source-free domain adaptation for multi-modal stroke lesion segmentation using shadowed sets and evidential uncertainty.Health information science and systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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 and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.