# EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2404.06181 · HTML: https://mlsanity.com/paper/2404.06181/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 56 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 30 of 35)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Learning with Pseudo-Ensembles](https://mlsanity.com/paper/1412.4864/) (2014)
- [Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels](https://mlsanity.com/paper/2203.03884/) (2022)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation](https://mlsanity.com/paper/1810.09091/) (2018)
- [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)
- [Dash: Semi-Supervised Learning with Dynamic Thresholding](https://mlsanity.com/paper/2109.00650/) (2021)
- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [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)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2203.01324/) (2022)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations](https://mlsanity.com/paper/1911.01218/) (2019)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training](https://mlsanity.com/paper/1811.12506/) (2018)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [Uncertainty Estimation by Fisher Information-based Evidential Deep Learning](https://mlsanity.com/paper/2303.02045/) (2023)
- [Semi-supervised Semantic Segmentation with Error Localization Network](https://mlsanity.com/paper/2204.02078/) (2022)

## Built on by (most cited first, 4 of 4)

- [Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images](https://mlsanity.com/paper/2501.01072/) (2025)
- [Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.14343/) (2025)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2507.19418/) (2025)

## Abstract (arXiv)

Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously. To address the aforementioned issues, we propose Evidential Prototype Learning (EPL), which utilizes an extended probabilistic framework to effectively fuse voxel probability predictions from different sources and achieves prototype fusion utilization of labeled and unlabeled data under a generalized evidential framework, leveraging voxel-level dual uncertainty masking. The uncertainty not only enables the model to self-correct predictions but also improves the guided learning process with pseudo-labels and is able to feed back into the construction of hidden features. The method proposed in this paper has been experimented on LA, Pancreas-CT and TBAD datasets, achieving the state-of-the-art performance in three different labeled ratios, which strongly demonstrates the effectiveness of our strategy.