Adversarial EM For Partially-Supervised Image-Quality Enhancement: Application To Low-Dose Pet Imaging

Abstract

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For image-quality enhancement, typical deep neural networks (DNNs) use large training sets and full supervision, but they generalize poorly to out-of-distribution (OOD) images exhibiting degradations absent during training. Also, having pairs of corresponding images at the desired quality and low quality becomes infeasible in many scenarios in medical image analysis. We propose a novel adversarial-learning framework for DNN-based image-quality enhancement which also incorporates variational modeling in latent space using expectation maximization (EM). Our EM framework extends to partially supervised learning that relaxes the quality requirement for reference images-used for DNN-loss computation during training-to a range in between the input/low quality and the desired/high quality. Results on two public datasets of positron-emission tomography show our framework’s benefits in generalizing to OOD images and visualizing DNN-output uncertainty while learning without full supervision.