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Image-to-image translation plays a vital role in tackling various medicalimaging tasks such as attenuation correction, motion correction, undersampledreconstruction, and denoising. Generative adversarial networks have been shownto achieve the state-of-the-art in generating high fidelity images for thesetasks. However, the state-of-the-art GAN-based frameworks do not estimate theuncertainty in the predictions made by the network that is essential for makinginformed medical decisions and subsequent revision by medical experts and hasrecently been shown to improve the performance and interpretability of themodel. In this work, we propose an uncertainty-guided progressive learningscheme for image-to-image translation. By incorporating aleatoric uncertaintyas attention maps for GANs trained in a progressive manner, we generate imagesof increasing fidelity progressively. We demonstrate the efficacy of our modelon three challenging medical image translation tasks, including PET to CTtranslation, undersampled MRI reconstruction, and MRI motion artefactcorrection. Our model generalizes well in three different tasks and improvesperformance over state of the art under full-supervision and weak-supervisionwith limited data. Code is released here:https://github.com/ExplainableML/UncerGuidedI2I

Uncertainty-Guided Progressive GANs for Medical Image Translation