Uncertainty-Guided Progressive GANs for Medical Image Translation

TL;DR

This paper introduces an uncertainty-guided progressive GAN framework for medical image translation, enhancing image quality and interpretability across multiple tasks by incorporating aleatoric uncertainty as attention maps.

Contribution

It proposes a novel uncertainty-guided progressive learning scheme for GANs that improves medical image translation performance and interpretability, especially with limited data.

Findings

Improves image fidelity in PET to CT translation

Enhances MRI reconstruction quality under limited supervision

Generalizes well across diverse medical imaging tasks

Abstract

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