# Evidential fully convolutional network for semantic segmentation

arXiv: https://arxiv.org/abs/2103.13544 · HTML: https://mlsanity.com/paper/2103.13544/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Decision-Making with Belief Functions: a Review](https://mlsanity.com/paper/1808.05322/) (2018)
- [Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective](https://mlsanity.com/paper/1807.01846/) (2018)

## Built on by (most cited first, 11 of 11)

- [Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation](https://mlsanity.com/paper/2208.06038/) (2022)
- [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)
- [An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers](https://mlsanity.com/paper/2208.00647/) (2022)
- [An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.07032/) (2024)
- [Fusion of evidential CNN classifiers for image classification](https://mlsanity.com/paper/2108.10233/) (2021)
- [Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation](https://mlsanity.com/paper/2208.00775/) (2022)
- [Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation](https://mlsanity.com/paper/2309.05919/) (2023)
- [DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.14444/) (2024)
- [A comparative study of attention mechanism and generative adversarial network in facade damage segmentation](https://mlsanity.com/paper/2209.13283/) (2022)
- [IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks](https://mlsanity.com/paper/2210.02637/) (2022)

## Abstract (arXiv)

We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation. In the so-called evidential FCN (E-FCN), an encoder-decoder architecture first extracts pixel-wise feature maps from an input image. A Dempster-Shafer layer then computes mass functions at each pixel location based on distances to prototypes. Finally, a utility layer performs semantic segmentation from mass functions and allows for imprecise classification of ambiguous pixels and outliers. We propose an end-to-end learning strategy for jointly updating the network parameters, which can make use of soft (imprecise) labels. Experiments using three databases (Pascal VOC 2011, MIT-scene Parsing and SIFT Flow) show that the proposed combination improves the accuracy and calibration of semantic segmentation by assigning confusing pixels to multi-class sets.