# Co-Evidential Fusion with Information Volume for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2506.02492 · HTML: https://mlsanity.com/paper/2506.02492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 73 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 30 of 37)

- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images](https://mlsanity.com/paper/2007.10732/) (2020)
- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency](https://mlsanity.com/paper/2012.07042/) (2020)
- [Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2203.01324/) (2022)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Uncertainty Estimation by Fisher Information-based Evidential Deep Learning](https://mlsanity.com/paper/2303.02045/) (2023)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2007.09854/) (2020)
- [The Fully Convolutional Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2206.00566/) (2022)
- [Revisit Self-Debugging with Self-Generated Tests for Code Generation](https://mlsanity.com/paper/2501.12793/) (2025)
- [Semantic Segmentation with Active Semi-Supervised Learning](https://mlsanity.com/paper/2203.10730/) (2022)
- [Decision under Uncertainty](https://mlsanity.com/paper/1304.1527/) (2013)
- [Belief function-based semi-supervised learning for brain tumor segmentation](https://mlsanity.com/paper/2102.00097/) (2021)
- [Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.10989/) (2021)
- [Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression](https://mlsanity.com/paper/2306.06760/) (2023)
- [Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting](https://mlsanity.com/paper/2405.06419/) (2024)
- [Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization](https://mlsanity.com/paper/2412.19418/) (2024)
- [MMGET: A Markov model for generalized evidence theory](https://mlsanity.com/paper/2105.07952/) (2021)
- [A Matrix-based Distance of Pythagorean Fuzzy Set and its Application in Medical Diagnosis](https://mlsanity.com/paper/2102.01538/) (2021)
- [EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.08988/) (2023)
- [Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.06177/) (2024)

## Abstract (arXiv)

Although existing semi-supervised image segmentation methods have achieved good performance, they cannot effectively utilize multiple sources of voxel-level uncertainty for targeted learning. Therefore, we propose two main improvements. First, we introduce a novel pignistic co-evidential fusion strategy using generalized evidential deep learning, extended by traditional D-S evidence theory, to obtain a more precise uncertainty measure for each voxel in medical samples. This assists the model in learning mixed labeled information and establishing semantic associations between labeled and unlabeled data. Second, we introduce the concept of information volume of mass function (IVUM) to evaluate the constructed evidence, implementing two evidential learning schemes. One optimizes evidential deep learning by combining the information volume of the mass function with original uncertainty measures. The other integrates the learning pattern based on the co-evidential fusion strategy, using IVUM to design a new optimization objective. Experiments on four datasets demonstrate the competitive performance of our method.