# Evidence fusion with contextual discounting for multi-modality medical image segmentation

arXiv: https://arxiv.org/abs/2206.11739 · HTML: https://mlsanity.com/paper/2206.11739/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation](https://mlsanity.com/paper/1711.01468/) (2017)
- [Lymphoma segmentation from 3D PET-CT images using a deep evidential network](https://mlsanity.com/paper/2201.13078/) (2022)
- [Evidential segmentation of 3D PET/CT images](https://mlsanity.com/paper/2104.13293/) (2021)
- [Covid-19 classification with deep neural network and belief functions](https://mlsanity.com/paper/2101.06958/) (2021)
- [Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation](https://mlsanity.com/paper/2108.05422/) (2021)

## Built on by (most cited first, 12 of 12)

- [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)
- [Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model](https://mlsanity.com/paper/2410.23905/) (2024)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI](https://mlsanity.com/paper/2302.00517/) (2023)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging](https://mlsanity.com/paper/2303.13033/) (2023)
- [An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.07032/) (2024)
- [Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation](https://mlsanity.com/paper/2309.05919/) (2023)
- [Evidence-aware multi-modal data fusion and its application to total knee replacement prediction](https://mlsanity.com/paper/2303.13810/) (2023)
- [Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation](https://mlsanity.com/paper/2403.02074/) (2024)
- [SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images](https://mlsanity.com/paper/2604.15777/) (2026)

## Abstract (arXiv)

As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep framework allowing us to merge multi-MR image segmentation results using the formalism of Dempster-Shafer theory while taking into account the reliability of different modalities relative to different classes. The framework is composed of an encoder-decoder feature extraction module, an evidential segmentation module that computes a belief function at each voxel for each modality, and a multi-modality evidence fusion module, which assigns a vector of discount rates to each modality evidence and combines the discounted evidence using Dempster's rule. The whole framework is trained by minimizing a new loss function based on a discounted Dice index to increase segmentation accuracy and reliability. The method was evaluated on the BraTs 2021 database of 1251 patients with brain tumors. Quantitative and qualitative results show that our method outperforms the state of the art, and implements an effective new idea for merging multi-information within deep neural networks.