mlsanityData to 7 Oct 2026

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

arXiv 2102.00097 ↗First posted 29 Jan 2021cs.CVcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 29 Jan 2021.

Builds on

10 of 22 references.

References · 10

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  3. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 138
  4. 2018No New-Netcited by 61
  5. 2019Deep Co-Training for Semi-Supervised Image Segmentationcited by 58
  6. 2018Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learningcited by 36
  7. 2017Deep GrabCut for Object Selectioncited by 33
  8. 20193D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRIcited by 28
  9. 2020Brain tumor segmentation with missing modalities via latent multi-source correlation representationcited by 19
  10. 2017Semi-Supervised Deep Learning for Fully Convolutional Networkscited by 11

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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 1; 2025: 2.

11 papers.

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  2. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  3. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  4. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  5. 2022Lymphoma segmentation from 3D PET-CT images using a deep evidential networkcited by 12
  6. 2024An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentationcited by 5
  7. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  8. 2021Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentationcited by 2
  9. 2023Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentationcited by 2
  10. 2025Mutual Evidential Deep Learning for Medical Image Segmentationcited by 0
  11. 2025Co-Evidential Fusion with Information Volume for Medical Image Segmentationcited by 0