mlsanityData to 7 Oct 2026

Lymphoma segmentation from 3D PET-CT images using a deep evidential network

arXiv 2201.13078 ↗First posted 31 Jan 2022cs.CVcited by 12

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Abstract (arXiv)

An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The feature extraction module uses an encoder-decoder framework to extract semantic feature vectors from 3D inputs. The evidential layer then uses prototypes in the feature space to compute a belief function at each voxel quantifying the uncertainty about the presence or absence of a lymphoma at this location. Two evidential layers are compared, based on different ways of using distances to prototypes for computing mass functions. The whole model is trained end-to-end by minimizing the Dice loss function. The proposed combination of deep feature extraction and evidential segmentation is shown to outperform the baseline UNet model as well as three other state-of-the-art models on a dataset of 173 patients.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 63 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

24 of 63 references.

References · 24

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12 more
  1. 2018Bayesian Layers: A Module for Neural Network Uncertaintycited by 57
  2. 2020Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessmentcited by 26
  3. 2020Post Training Uncertainty Calibration of Deep Networks For Medical Image Segmentationcited by 19
  4. 2021An evidential classifier based on Dempster-Shafer theory and deep learningcited by 18
  5. 2018Decision-Making with Belief Functions: a Reviewcited by 13
  6. 2018Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspectivecited by 11
  7. 2021Belief function-based semi-supervised learning for brain tumor segmentationcited by 11
  8. 2021Evidential fully convolutional network for semantic segmentationcited by 11
  9. 2021Evidential segmentation of 3D PET/CT imagescited by 8
  10. 2020NN-EVCLUS: Neural Network-based Evidential Clusteringcited by 5
  11. 2021Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentationcited by 5
  12. 2019DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functionscited by 4

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Cited by year

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

12 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 quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  3. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  4. 2022Evidence fusion with contextual discounting for multi-modality medical image segmentationcited by 12
  5. 2022An Evidential Neural Network Model for Regression Based on Random Fuzzy Numberscited by 6
  6. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  7. 2024Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimescited by 3
  8. 2025Uncertainty-Supervised Interpretable and Robust Evidential Segmentationcited by 3
  9. 2023Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentationcited by 2
  10. 2024Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Networkcited by 1
  11. 2024Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentationcited by 1
  12. 2025UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomographycited by 0