# Uncertainty-Guided Progressive GANs for Medical Image Translation

arXiv: https://arxiv.org/abs/2106.15542 · HTML: https://mlsanity.com/paper/2106.15542/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Image De-raining Using a Conditional Generative Adversarial Network](https://mlsanity.com/paper/1701.05957/) (2017)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition](https://mlsanity.com/paper/1902.03051/) (2019)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Perceptual Adversarial Networks for Image-to-Image Transformation](https://mlsanity.com/paper/1706.09138/) (2017)
- [Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation](https://mlsanity.com/paper/1810.07884/) (2018)
- [Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution](https://mlsanity.com/paper/1705.00664/) (2017)
- [Robust Super-Resolution GAN, with Manifold-based and Perception Loss](https://mlsanity.com/paper/1903.06920/) (2019)
- [Uncertainty-aware Generalized Adaptive CycleGAN](https://mlsanity.com/paper/2102.11747/) (2021)
- [Synthesizing Filamentary Structured Images with GANs](https://mlsanity.com/paper/1706.02185/) (2017)

## Built on by (most cited first, 16 of 16)

- [BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks](https://mlsanity.com/paper/2207.06873/) (2022)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models](https://mlsanity.com/paper/2307.00398/) (2023)
- [Semi-Supervised Disease Classification based on Limited Medical Image Data](https://mlsanity.com/paper/2405.04295/) (2024)
- [USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution](https://mlsanity.com/paper/2305.17520/) (2023)
- [Likelihood Annealing: Fast Calibrated Uncertainty for Regression](https://mlsanity.com/paper/2302.11012/) (2023)
- [CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach](https://mlsanity.com/paper/2410.21932/) (2024)
- [Can virtual staining for high-throughput screening generalize?](https://mlsanity.com/paper/2407.06979/) (2024)
- [What can we learn about a generated image corrupting its latent representation?](https://mlsanity.com/paper/2210.06257/) (2022)
- [HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms](https://mlsanity.com/paper/2311.13821/) (2023)
- [Uncertainty-Aware Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2412.01705/) (2024)
- [Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation](https://mlsanity.com/paper/2510.19679/) (2025)
- [Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models](https://mlsanity.com/paper/2211.06250/) (2022)
- [Uncertainty-aware Test-Time Training (UT3) for Efficient On-the-fly Domain Adaptive Dense Regression](https://mlsanity.com/paper/2509.03012/) (2025)
- [Uncertainty-guided Generation of Dark-field Radiographs](https://mlsanity.com/paper/2601.15859/) (2026)
- [A Proper Scoring Rule for Virtual Staining](https://mlsanity.com/paper/2602.23305/) (2026)

## Abstract (arXiv)

Image-to-image translation plays a vital role in tackling various medical imaging tasks such as attenuation correction, motion correction, undersampled reconstruction, and denoising. Generative adversarial networks have been shown to achieve the state-of-the-art in generating high fidelity images for these tasks. However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model. In this work, we propose an uncertainty-guided progressive learning scheme for image-to-image translation. By incorporating aleatoric uncertainty as attention maps for GANs trained in a progressive manner, we generate images of increasing fidelity progressively. We demonstrate the efficacy of our model on three challenging medical image translation tasks, including PET to CT translation, undersampled MRI reconstruction, and MRI motion artefact correction. Our model generalizes well in three different tasks and improves performance over state of the art under full-supervision and weak-supervision with limited data. Code is released here: https://github.com/ExplainableML/UncerGuidedI2I