# Belief function-based semi-supervised learning for brain tumor segmentation

arXiv: https://arxiv.org/abs/2102.00097 · HTML: https://mlsanity.com/paper/2102.00097/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning](https://mlsanity.com/paper/1810.12241/) (2018)
- [Deep GrabCut for Object Selection](https://mlsanity.com/paper/1707.00243/) (2017)
- [3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/1904.03355/) (2019)
- [Brain tumor segmentation with missing modalities via latent multi-source correlation representation](https://mlsanity.com/paper/2003.08870/) (2020)
- [Semi-Supervised Deep Learning for Fully Convolutional Networks](https://mlsanity.com/paper/1703.06000/) (2017)

## Built on by (most cited first, 11 of 11)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [TBraTS: Trusted Brain Tumor Segmentation](https://mlsanity.com/paper/2206.09309/) (2022)
- [Lymphoma segmentation from 3D PET-CT images using a deep evidential network](https://mlsanity.com/paper/2201.13078/) (2022)
- [An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.07032/) (2024)
- [Application of belief functions to medical image segmentation: A review](https://mlsanity.com/paper/2205.01733/) (2022)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation](https://mlsanity.com/paper/2309.05919/) (2023)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [Co-Evidential Fusion with Information Volume for Medical Image Segmentation](https://mlsanity.com/paper/2506.02492/) (2025)

## Abstract (arXiv)

Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However, obtaining precisely annotated data is very challenging in the medical domain. Moreover, labeling uncertainty and imprecision make segmentation results unreliable. In this paper, we address the uncertain boundary problem by a new evidential neural network with an information fusion strategy, and the scarcity of annotated data by semi-supervised learning. Experimental results show that our proposal has better performance than state-of-the-art methods.