# Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training

arXiv: https://arxiv.org/abs/2301.13296 · HTML: https://mlsanity.com/paper/2301.13296/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging](https://mlsanity.com/paper/2405.02918/) (2024)
- [Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models](https://mlsanity.com/paper/2507.15976/) (2025)
- [Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Training](https://mlsanity.com/paper/2405.06487/) (2024)
- [Enhancing Self-Driving Segmentation in Adverse Weather Conditions: A Dual Uncertainty-Aware Training Approach to SAM Optimization](https://mlsanity.com/paper/2509.04735/) (2025)
- [Multi-Rater Calibrated Segmentation Models](https://mlsanity.com/paper/2605.02437/) (2026)

## Abstract (arXiv)

In terms of accuracy, deep learning (DL) models have had considerable success in classification problems for medical imaging applications. However, it is well-known that the outputs of such models, which typically utilise the SoftMax function in the final classification layer can be over-confident, i.e. they are poorly calibrated. Two competing solutions to this problem have been proposed: uncertainty-aware training and evidential neural networks (ENNs). In this paper, we perform an investigation into the improvements to model calibration that can be achieved by each of these approaches individually, and their combination. We perform experiments on two classification tasks: a simpler MNIST digit classification task and a more complex and realistic medical imaging artefact detection task using Phase Contrast Cardiac Magnetic Resonance images. The experimental results demonstrate that model calibration can suffer when the task becomes challenging enough to require a higher-capacity model. However, in our complex artefact detection task, we saw an improvement in calibration for both a low and higher-capacity model when implementing both the ENN and uncertainty-aware training together, indicating that this approach can offer a promising way to improve calibration in such settings. The findings highlight the potential use of these approaches to improve model calibration in a complex application, which would in turn improve clinician trust in DL models.