# Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion

arXiv: https://arxiv.org/abs/2311.12153 · HTML: https://mlsanity.com/paper/2311.12153/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation](https://mlsanity.com/paper/1805.08841/) (2018)
- [Continuously Differentiable Exponential Linear Units](https://mlsanity.com/paper/1704.07483/) (2017)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition](https://mlsanity.com/paper/1902.03051/) (2019)
- [Bayesian Learning for Neural Networks: an algorithmic survey](https://mlsanity.com/paper/2211.11865/) (2022)
- [Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement](https://mlsanity.com/paper/2110.03343/) (2021)
- [fRegGAN with K-space Loss Regularization for Medical Image Translation](https://mlsanity.com/paper/2303.15938/) (2023)

## Built on by (most cited first, 2 of 2)

- [BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives](https://mlsanity.com/paper/2403.07800/) (2024)
- [Uncertainty-Aware Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2412.01705/) (2024)

## Abstract (arXiv)

In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks. In this work, we focus on the estimation of epistemic uncertainty for 3D medical image-to-image translation. We propose a novel model uncertainty quantification method, Multi-Axis Fusion (MAF), which relies on the integration of complementary information derived from multiple views on volumetric image data. The proposed approach is applied to the task of synthesizing contrast enhanced T1-weighted images based on native T1, T2 and T2-FLAIR scans. The quantitative findings indicate a strong correlation (ρtext healthy = 0.89) between the mean absolute image synthetization error and the mean uncertainty score for our MAF method. Hence, we consider MAF as a promising approach to solve the highly relevant task of detecting synthetization failures at inference time.